1. A system (100) for real-time disaster prediction and situational intelligence, the system (100) comprising: a data acquisition module configured to collect heterogeneous data from a plurality of sources including satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and real-time communication streams; a preprocessing module configured to perform noise filtering, normalization, temporal alignment, and missing data handling on the collected data; a cross-modal data fusion engine configured to integrate the heterogeneous data using an attention-based weighting mechanism to generate a unified representation; a spatio-temporal digital twin module configured to generate a dynamic virtual representation of a monitored environment and simulate disaster propagation over time; an adaptive artificial intelligence (AI) prediction module configured to analyze the fused data to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones; a generative reconstruction module configured to reconstruct incomplete or missing data and simulate hypothetical disaster scenarios; a situational intelligence generation module configured to generate outputs including hazard maps, risk classifications, and predictive alerts; a feedback and adaptive learning module configured to detect model drift and perform selective retraining based on real-time and historical data; and a user interface and decision support module configured to present actionable intelligence to users.

2. The system as claimed in claim 1, wherein the data acquisition module is configured to collect real-time environmental parameters including temperature, humidity, atmospheric pressure, seismic activity, water levels, and wind patterns.

3. The system as claimed in claim 1, wherein the cross-modal data fusion engine utilizes transformer-based attention mechanisms to dynamically prioritize data sources based on contextual relevance.

4. The system as claimed in claim 1, wherein the adaptive AI prediction module comprises one or more models selected from convolutional neural networks (CNNs), represent neural networks (RNNs), long short-term memory (LSTM) networks, transformer models, and graph neural networks (GNNs).

5. The system as claimed in claim 1, wherein the feedback and adaptive learning module is configured to implement federated learning by aggregating anonymized model updates from distributed edge devices.

6. A method for real-time disaster prediction and situational intelligence, the method comprising: collecting heterogeneous data from a plurality of sources including satellite imagery, UAV systems, IoT sensors, and communication streams; preprocessing the collected data by performing noise filtering, normalization, temporal alignment, and missing data handling; extracting features and performing cross-modal data fusion using attention-based mechanisms to generate a unified representation; generating a spatio-temporal digital twin of a monitored environment and simulating disaster propagation; applying adaptive artificial intelligence models to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones; reconstructing incomplete data and simulating scenarios using generative models; generating situational intelligence outputs including hazard maps, risk classifications, and alerts; assigning severity levels and confidence scores to predictions; and transmitting alerts and intelligence outputs to one or more users or systems.

7. The method as claimed in claim 6, wherein the step of feature extraction includes deriving spatial, temporal, and environmental features including anomaly indicators and correlation patterns across multiple data modalities.

8. The method as claimed in claim 6, wherein the step of applying adaptive artificial intelligence models includes using deep learning architectures for spatial analysis, temporal prediction, and graph-based propagation modeling.

9. The method as claimed in claim 6, further comprising generating dynamic hazard maps and time-evolving risk zones based on predicted disaster progression.

10. The method as claimed in claim 6, further comprising updating the artificial intelligence models using feedback-based adaptive learning and federated learning without transmitting raw data to a central server.

Description:TECHNICAL FIELD
[0001] The present invention relates to disaster management and intelligent monitoring systems, and more specifically, to a system and method for an adaptive multi-modal artificial intelligence (AI) engine for real-time disaster prediction and situational intelligence.
BACKGROUND
[0002] In recent years, there has been a growing interest for advanced disaster management systems that can use artificial intelligence (AI) to improve monitoring, prediction, and response to both natural and man-made disasters in recent years. Conventional ways of managing disasters often use data from only one source, report it late, and analyze it by hand. This makes it hard to understand what's going on and makes it hard to make decisions quickly when things go wrong.
[0003] Existing disaster prediction systems depend on only one type of input, like satellite images or localized sensor data. They also have trouble on capturing the, multi-dimensional nature disaster environments. These kinds of systems lack on combining different types of data, like satellite images, UAV data, IoT sensor inputs, and real-time communication feeds. This makes predictions less accurate and timely.
[0004] The complexity of disaster environments, are complex and change over time and space makes them very difficult to work with. It's hard for conventional data processing and modeling methods to effectively link different data sources, which makes analyses less complete, forecasts less accurate, and increases the chances of false alarms or missing disaster events.
[0005] Additionally, there is also a limitation on making that data acquisition systems, analytical models, and communication infrastructures work collectively smooth also for making sure that data can be sent in real time, stored safely, processed quickly, and used on different platforms is a big problem, especially in large-scale and quickly changing disaster situations.
[0006] Addressing these challenges requires a system and method that combines multi-modal sensing technologies, advanced AI/ML-based predictive models, and efficient communication frameworks. This will allow for continuous monitoring, accurate disaster prediction, and the creation of real-time situational intelligence for emergency responders and decision-makers.
[0007] For example, existing systems use satellite observations or sensor-driven predictive models to monitor disasters, but they don't offer a unified, adaptable framework that can combine different types of data, perform dynamic multi-modal fusion, and create comprehensive real-time situational intelligence with predictive capabilities.
[0008] Likewise, certain systems also talk about using UAVs to help with monitoring and sending data for disaster assessment, but they can't do advanced AI-driven multi-source data fusion, simulate how a disaster will progress, or give adaptive learning mechanisms to make predictions more accurate over time.
[0009] Conventional disaster management depend on late reporting and broken-up data analysis, which might miss early warning signs of disaster events. Reading data by hand takes a lot of time, is prone to mistakes, and can't be done on a large scale for continuous real-time monitoring across large areas.
[0010] Existing systems that depend on a small amount of data can't give a full picture of disaster situations. Also, not having real-time analytics and predictive intelligence makes it harder to take proactive measures and respond quickly.
[0011] The limitations of conventional methods are that they can't integrate data from multiple sources, they can't process dynamic and heterogeneous data in real time, they don't have accurate predictions, they don't have adaptive learning mechanisms, and their outputs are either delayed or not useful. All of these problems make it harder to prepare for, respond to, and reduce the effects of disasters, which raises the risks and damages that could happen.
[0012] The present invention solves these problems by offering a system and method for an adaptive multi-modal artificial intelligence (AI) engine that combines data from different sources, performs advanced data fusion, makes real-time disaster predictions, and creates dynamic situational intelligence outputs for proactive decision-making and quick emergency response.
[0013] Further limitations of conventional approaches will become apparent through comparison with the embodiments of the present inventions presented in the detailed description and accompanying drawings.
SUMMARY
[0014] The summary provided herein is illustrative only and is not intended to be limiting in any Manne. The drawings and detailed description that follow will show more examples, improvements, and variations of the present invention, in addition to the ones already mentioned. The present invention includes a number of different ways to set up a system and method for an adaptive multi-modal artificial intelligence (AI) engine that can predict disasters in real time and give situational intelligence.
[0015] The present invention shows the full system and method for predicting disasters in real time and getting information about the situation. It do with the help of an adaptive multi-modal artificial intelligence (AI) engine that combines different kinds of data and advanced analytics. The system is designed to work with combination of data from satellite images, drones, IoT sensors, and real-time communication streams. It can also generate, sort, and predict disaster events in real time. The AI-driven framework uses deep learning and generative AI for not only find the unusual events and early warning signs, but also to predict how a disaster will unfold, how savor it will be, and where it will have the biggest impact.
[0016] So, the present invention introduces a proactive disaster intelligence system which capable of real-time data collection, integration, and collection of multi-dimensional environmental data. A multi-modal data acquisition unit, a preprocessing and normalization module, a cross-modal data fusion engine, an adaptive AI prediction module, a generative reconstruction module for working with incomplete data, a situational intelligence generation module, a feedback and adaptive learning module, and a user interface and decision support module are some of the most important example parts of the system. When these parts work together, they make a smart, scalable, and dependable platform for predicting and responding to disasters.
[0017] The system continuously acquires and processes data like temperature, humidity, seismic activity, atmospheric pressure, water levels, wind patterns, and geospatial images. The preprocessing module checks that the inputs are good by removing noise, normalizing them, aligning them over time, and filling in any gaps in the data. The cross-modal fusion engine combines spatial, temporal, and contextual features from different data streams using advanced fusion methods. This helps you understand complicated disaster situations. The adaptive AI prediction module uses data from different sources to find patterns, recognise when a disaster will happen, guess how it will progress, and give seviour scores.
[0018] In an advanced embodiment, the system has a spatio-temporal digital twin simulation module that makes a dynamic virtual version of the monitored environment in more advanced manner. the present module shows how disasters like floods, wildfires, or areas affected by earthquakes can spread over time. This helps with planning ahead and seeing what might happen. A generative AI module also fills in missing or incomplete data and makes up fake disaster scenarios to make the system more accurate and stronger when things aren't clear.
[0019] The situational intelligence generation module produces outputs including time- evaluating hazard monitoring, risk-level assessment, predict alerts, and decision-support analytics. Alerts are generated based on severity and confidence levels, which enabling context-aware escalation ranging from advisory notifications to emergency warnings. These outputs are delivered in real time to emergency responders, authorities, and stakeholders through intuitive user interfaces and integrated communication systems.
[0020] The feedback and adaptive learning unit enables system improvement by incorporating real-time data, historical records, and user feedback to detect model drift and trigger selective retraining of AI models. This closed-loop adaptive mechanism ensures that the system maintains high accuracy, responsiveness, and robustness in dynamically changing disaster environments. The system also further employ edge–cloud distributed processing, wherein initial data processing and inference occur at edge devices, such as UAVs or IoT nodes, while advanced analytics and simulation are performed in centralized or cloud-based infrastructure.
[0021] In an embodiment, a method for real-time disaster prediction and situational intelligence is disclosed. The process includes collecting different kinds of data from different places, cleaning and normalizing the data, finding features and putting them together in different ways, using adaptive AI models to predict disasters and how they will unfold, making situational intelligence outputs like hazard maps and alerts, and always updating the predictive models with feedback and adaptive learning methods. The method also includes making alerts that are based on how confident you are and changing the prediction parameters in real time based on how the environment changes.
[0022] In another embodiment a way to combine disaster prediction with situational intelligence. The system has sensing units that can get data from many different places, a processing unit that can combine data, make predictions using AI, and run simulations, and a communication system that can send alerts and intelligence outputs in real time. The system makes sure that people get and send alerts for certain disasters, like flood warnings, wildfire alerts, and notifications of seismic risk, in real time. This speeds up response times, lowers uncertainty, and lets people plan ahead for disasters and deal with them better.
BRIEF DESCRIPTION OF DRAWINGS
[0023] The accompanying drawings illustrate various embodiments of the disclosed system and method for an adaptive multi-modal artificial intelligence (AI) engine for real-time disaster prediction and situational intelligence. It will be understood by persons of ordinary skill in the art that the illustrated boundaries (e.g., modules, blocks, or functional units) represent one example of the logical and/or physical organization of components. In certain embodiments, a single module may be implemented as multiple distributed components, or multiple illustrated modules may be combined into a unified implementation. Further, components illustrated as internal to a system may, in alternative embodiments, be implemented externally or in a distributed computing environment such as edge or cloud infrastructure. The drawings are schematic in nature and are not necessarily drawn to scale.
[0024] Various embodiments of the present invention will hereinafter be described with reference to the appended drawings, which are intended to illustrate, without limitation, the scope, structure, and functionality of the disclosed system and method. Like reference numerals are used to denote similar or corresponding elements throughout the drawings for consistency and clarity.
[0025] Figure 1 is a block diagram illustrating the overall system architecture of the adaptive multi-modal AI engine, in accordance with an embodiment of the present invention.
[0026] Figure 2 is a block diagram illustrating a distributed data acquisition and processing environment, in accordance with an embodiment of the present invention.
[0027] Figure 3 is a flowchart illustrating a method for real-time disaster prediction and situational intelligence, in accordance with an embodiment of the present invention.
[0028] Figure 4 is a block diagram illustrating an advanced multi-modal fusion and prediction module is shown, in accordance with an embodiment of the present invention.
[0029] Figure 5 is a flowchart illustrating an adaptive AI/ML-based prediction and learning method is shown, in accordance with an embodiment of the present invention.
[0030] It is to be understood that the number and arrangement of drawings are exemplary and may be varied, combined, or expanded as required to effectively describe and enable the various embodiments of the present invention without departing from the scope of the disclosure.
DETAILED DESCRIPTION
[0031] The present invention may be best understood with reference to the detailed description and the accompanying drawings described herein. Various embodiments of a system and method for an adaptive multi-modal artificial intelligence (AI) engine (100) for real-time disaster prediction and situational intelligence are discussed with reference to the figures. However, it will be appreciated by those skilled in the art that the detailed descriptions are exemplary in nature, and the disclosed system and method may be extended, modified, or implemented in alternative configurations depending on specific operational requirements, deployment environments, and disaster scenarios, without departing from the scope of the present invention.
[0032] References to “one embodiment,” “at least one embodiment,” “an embodiment,” “one example,” “an example,” “for example,” and similar expressions indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, or functional element; however, not every embodiment necessarily includes the same set of features. Accordingly, such terms should not be interpreted as limiting, and repeated use of such expressions does not imply reference to the same embodiment unless explicitly stated.
[0033] A primary objective of the present inventions to use satellite images, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensor networks, and real-time communication streams to continuously monitor and analyze disaster-related parameters in real time. Another important goal is to analyze and connect multi-dimensional spatial, temporal, and environmental data to find strange patterns, group disasters into different types, and predict when they will happen, how they will get worse, and how bad they will be. This will help find potentially dangerous situations early and cut down on false alarms.
[0034] Another objective of the present inventions is to create dynamic and useful situational for emergency response. This includes time-evolving hazard maps, risk-level classifications, predictive insights, and decision-support. Another objective of the present invention is to send real-time alerts and notifications with mechanisms which take into account severity levels, prediction confidence, and possible impact zones. The system also make sure for current disaster management systems for authorities can quickly access processed intelligence, predictive reports, and geospatial visualizations. Secure transmission protocols and strong system architecture put data security, integrity, and reliable communication first, making sure that disaster prediction and response are quick, scalable, and trustworthy.
[0035] Collectively, these objectives work together to create a proactive and smart disaster management framework. the present framework allows for continuous monitoring of the environment in multiple situations like accurate detection and prediction of disaster events, early identification of high-risk situations, and informed real time situation for emergency responders and authorities. This improves response efficiency, reduces potential damage and loss, and makes people better prepared and more resilient for disasters.
[0036] Figure 1 illustrates a system environment (100) in which various embodiments of the system and method for an adaptive multi-modal artificial intelligence (AI) engine for real-time disaster prediction and situational intelligence may be implemented. The system environment (100) comprises a plurality of distributed data acquisition sources (102), one or more user or control devices (104), a communication network (106), and a centralized and/or distributed processing system (108). In an embodiment, the system (100) is configured to collect, process, analyze, and disseminate disaster-related intelligence through coordinated interaction between sensing infrastructure, computational modules, and user interfaces.
[0037] The plurality of data acquisition sources (102) includes heterogeneous sensing platforms to configured for capture real-time environmental parameter and contextual related data related to disaster monitor. These sources include, limitation of satellite-based remote sensing systems, unmanned aerial vehicles (UAVs) which equipped with imaging and environmental sensors, ground-based Internet of Things (IoT) sensor networks, and real-time communication feeds like social media streams, emergency communication channels, and telemetry systems. The IoT sensor networks comprise sensors configured to monitor parameters such as temperature, humidity, atmospheric pressure, seismic activity, water levels, wind speed, gas concentrations, and other environmental indicators. UAVs may capture high-resolution imagery and localized environmental data, while satellite systems provide large-scale geospatial observations.
[0038] The data acquisition sources (102) are configured to operate in a distributed and continuous manner, enabling real-time monitoring across large geographical regions. The collected data include structured, semi-structured, and unstructured formats, such as numerical sensor readings, image and video data, geospatial maps, and textual communication inputs. This multi-modal data is transmitted to the processing system (108) through the communication network (106) for further analysis and integration.
[0039] The processing system (108) comprises one or more computing units configured to implement the adaptive multi-modal AI engine (100). In an embodiment, the processing system includes modules for data preprocessing and normalization, cross-modal data fusion, adaptive AI-based prediction, generative data reconstruction, spatio-temporal digital twin simulation, situational intelligence generation, and feedback-driven learning. The preprocessing module performs operations such as noise filtering, data cleaning, normalization, and temporal alignment to ensure consistency across heterogeneous data sources. The fusion module integrates spatial, temporal, and contextual features using advanced fusion techniques to generate a unified representation of the monitored environment.
[0040] The adaptive AI prediction module within the processing system (108) employs deep learning and machine learning algorithms to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones. In certain embodiments, the system incorporates a digital twin simulation module that generates a virtual representation of the physical environment and simulates disaster propagation over time, enabling predictive visualization and scenario analysis. Additionally, a generative AI module may be utilized to reconstruct missing data and simulate potential disaster scenarios to improve robustness and predictive accuracy.
[0041] The system also has one or more user or control devices (104). These include things like tablets, smartphones, laptops, control terminals, and command center interfaces. These devices get situational intelligence outputs from the processing system (108). These outputs include real-time alerts, hazard maps, predictive analytics, and suggestions to help people make decisions. People like emergency responders, disaster management authorities, and stakeholders can use the devices' (104) user interface to see data, keep an eye on how disasters are changing, and start the right response actions.
[0042] The communication network (106) provides a medium for reliable and secure data transmission between the data acquisition sources (102), processing system (108), and user devices (104). The network may utilize a combination of wired and wireless communication protocols, including but not limited to TCP/IP, HTTP/HTTPS, MQTT, ZigBee, Bluetooth, Wi-Fi, cellular networks (2G to 6G), satellite communication systems, and other networking standards. The communication network (106) ensures low-latency data transfer, scalability, and interoperability across distributed system components.
[0043] In an embodiment, the system environment supports an edge-cloud hybrid architecture. This means that initial data processing and inference can happen at edge nodes connected to IoT devices or UAVs, while advanced analytics, data fusion, and simulation can happen at centralized or cloud-based processing units. This architecture makes the system more responsive, lowers latency, and makes it easier to handle large amounts of real-time data streams.
[0044] The system environment (100) as a whole is an integrated and scalable framework for real-time disaster prediction and situational intelligence. It does this by allowing distributed sensing platforms, advanced AI-driven processing modules, and user-centric decision support interfaces to work together smoothly. This makes proactive disaster management and quick response easier.
[0045] Figure 2 is a block diagram illustrating a processing and intelligence module (102) implementing the adaptive multi-modal artificial intelligence (AI) engine for real-time disaster prediction and situational intelligence, in accordance with an embodiment of the present disclosure. Figure 2 is described in conjunction with elements of Figure 1. In one embodiment, the processing module (102), operatively coupled with the processing system (108), comprises a processor (202), a memory (204), a transceiver (206), an input/output (I/O) unit (208), a plurality of data acquisition interfaces or sensor inputs (210), a machine learning and analytics unit (212), and an alert and decision support unit (214). The processor (202) is communicatively coupled with all system components, and the transceiver (206) is configured to interface with the communication network (106) for real-time data exchange.
[0046] The processor (202) includes logic, circuitry, and executable instructions configured to coordinate operations of the various modules within the system (102). The processor (202) manages data acquisition, preprocessing, multi-modal fusion, AI-driven prediction, simulation, and alert generation processes. The processor may be implemented using one or more computing architectures, including but not limited to general-purpose processors, reduced instruction set computing (RISC) processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or distributed processing units.
[0047] The memory (204) is configured to store processor-executable instructions, trained artificial intelligence and machine learning models, historical disaster data, geospatial datasets, and intermediate processing outputs. The memory (204) may include volatile and non-volatile storage components such as random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drives, or cloud-integrated storage systems. The memory may further maintain historical environmental data, disaster event records, and predictive model parameters to support longitudinal analysis and adaptive learning.
[0048] The transceiver (206) enables bi-directional communication between the processing module (102), distributed data acquisition sources (102), user devices (104), and the centralized processing system (108). It facilitates real-time transmission of raw data, processed insights, predictive outputs, and alert notifications. The transceiver (206) may also receive model updates, configuration parameters, and external inputs such as emergency communication feeds. In various embodiments, the transceiver (206) may support communication technologies including cellular networks (2G–6G), Wi-Fi (IEEE 802.11 standards), satellite communication, Bluetooth, ZigBee, and other wired or wireless protocols.
[0049] The input/output (I/O) unit (208) provides an interface for user interaction and system control. The I/O unit may include display interfaces, dashboards, visualization panels, control consoles, and input mechanisms such as keyboards, touch interfaces, or command systems. Through the I/O unit (208), users such as disaster management authorities or operators can monitor real-time data, visualize hazard maps, review predictive analytics, and issue commands or response actions.
[0050] The plurality of data acquisition interfaces or sensor inputs (210) is configured to receive heterogeneous data from multiple external sources, including satellite imagery, UAV-based sensors, IoT sensor networks, and real-time communication streams. These inputs may include structured data such as numerical sensor readings, as well as unstructured data such as images, videos, and textual feeds. The data acquisition interfaces (210) ensure continuous and synchronized ingestion of multi-modal data required for comprehensive disaster monitoring.
[0051] The machine learning and analytics unit (212) constitutes the core intelligence component of the system and is configured to implement advanced AI/ML models for disaster prediction and situational analysis. The unit (212) processes incoming data to extract features, perform cross-modal data fusion, detect anomalies, classify disaster types, and predict disaster onset and progression. In certain embodiments, the unit (212) incorporates deep learning architectures, attention-based fusion mechanisms, and generative AI models to reconstruct missing data and enhance predictive robustness.
[0052] The machine learning and analytics unit (212) may further include a spatio-temporal digital twin simulation capability, enabling the system to generate a virtual representation of the monitored environment and simulate disaster propagation scenarios over time. This enables prediction of hazard spread, estimation of impact zones, and evaluation of alternative response strategies. The unit (212) may also generate confidence scores associated with predictions to support reliability assessment and decision-making.
[0053] The machine learning and analytics unit (212) is additionally configured to perform adaptive learning by incorporating real-time data, historical patterns, and feedback inputs. It may detect model drift or performance degradation and trigger selective retraining or parameter updates, thereby maintaining accuracy and responsiveness under changing environmental conditions.
[0054] The alert and decision support unit (214) is configured to generate and disseminate real-time alerts, warnings, and recommendations based on outputs from the machine learning and analytics unit (212). Alerts may be categorized based on severity levels, confidence scores, and predicted impact, and may include advisory notifications, precautionary warnings, and emergency escalation alerts. The unit (214) ensures that relevant information is delivered to appropriate stakeholders, including emergency responders, authorities, and affected populations.
[0055] The alert and decision support unit (214) provide for actionable recommendations, such as evacuation advisories, resource allocation strategies, and mitigation measures, based on predictive insights and simulation outputs. These recommendations may be dynamically updated as new data is received and processed.
[0056] In operation, the processing module (102) continuously receives multi-modal data through the data acquisition interfaces (210), to processes the data using machine learning and analytics unit (212), and generates situational intelligence outputs and alerts through the alert unit (214). The system operates in real time, which enabling continuous monitoring and rapid response to evolve disaster scenarios.
[0057] The transceiver (206) communication between both raw and processed data, while the memory (204) store historical records and model parameters for adaptive learning. The processor (202) coordinates all operations to ensure efficient and synchronized functioning of the system components.
[0058] Overall, the processing module (102) combines multi-source data acquisition unit, and advanced AI-driven analytics, and real-time communication which capable to provide a comprehensive and proactive disaster prediction and situational intelligence system, thereby significantly enhancing disaster preparedness, response efficiency, and risk mitigation.
[0059] In one embodiment, the present system is being used in a place where disasters are likely to happen. The adaptive multi-modal artificial intelligence (AI) engine (100) gets data from satellite images, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and real-time communication streams all the time. When the system sees early signs of a possible disaster, like an unusual rise in water levels, seismic activity, or sudden temperature spikes that could mean a wildfire, it uses machine learning and analytics to process incoming data in real time. The AI engine sees the event as a major disaster and sends out a high-priority alert right away. The system makes geospatial hazard maps with time stamps, predicts how disasters will get worse, and sends the data it has analyzed to user devices and central control systems. The processing system also adds information about the location and sends an alert to emergency response agencies and other relevant agencies so that they can take quick action and evacuate or take other steps to reduce the damage.
[0060] In another embodiment, when the system detects a moderate-risk or developing disaster condition, such as gradual increase in river levels or localized environmental anomalies, the system generates context-aware advisory alerts instead of full emergency escalation. These alerts include predictive insights, risk-level classifications, and recommended precautionary actions such as monitoring specific zones, resource pre-positioning, or issuing early warnings to nearby populations. The system stores processed data and predictive outputs within the memory and cloud infrastructure for longitudinal analysis and future model improvement. By dynamically adjusting alert levels based on severity, confidence scores, and predicted impact, the system ensures that notifications remain actionable, relevant, and proportional, thereby reducing false alarms and enhancing operational efficiency.
[0061] In one embodiment, the system (100) comprises a processing unit including a processor operatively coupled with a memory and a plurality of data acquisition interfaces configured to receive multi-modal inputs. The memory stores processor-executable instructions which, when executed, cause the processor to acquire environmental and contextual data, perform preprocessing operations such as filtering and temporal alignment, and extract relevant spatial, temporal, and environmental features. The processor further applies trained artificial intelligence and machine learning models to classify disaster types, predict onset and progression, and generate confidence-weighted outputs. The system may further incorporate a spatio-temporal digital twin module configured to simulate disaster evolution and assess potential impact zones under varying conditions.
[0062] Based on the predictive analysis, the system generates real-time alerts and situational intelligence outputs tailored to the severity and type of disaster event. Alerts may include advisory notifications, warning alerts, or emergency escalation messages, and are communicated to authorities, emergency responders, and stakeholders through visual dashboards, communication networks, or integrated control systems. The system may further transmit geospatial intelligence, predictive reports, and annotated hazard maps to assist in decision-making and resource allocation. Historical data is stored locally and/or in cloud-based systems to enable continuous learning and improvement of predictive models. Additionally, the system ensures secure transmission and storage of data using robust encryption protocols, thereby maintaining data integrity, confidentiality, and reliability across all operational stages.
[0063] Figure 3 is a flowchart illustrating a method for real-time disaster prediction and situational intelligence using the adaptive multi-modal artificial intelligence (AI) engine (100), in accordance with an embodiment of the present disclosure. The method begins at a start step (300) and proceeds through a sequence of operations involving multi-source data acquisition, preprocessing, data fusion, predictive analytics, alert generation, and adaptive learning. The method is designed to operate continuously in real time to enable proactive monitoring and response to disaster events.
[0064] At step (302), the system (100), through a plurality of data acquisition interfaces (210), continuously collects heterogeneous data from multiple sources including satellite imagery, unmanned aerial vehicles (UAVs), IoT sensor networks, and real-time communication streams. The collected data may include environmental parameters such as temperature, humidity, atmospheric pressure, seismic activity, water levels, wind speed, gas concentrations, and geospatial imagery, along with unstructured inputs such as video feeds and textual communication data relevant to disaster monitoring.
[0065] At step (304), the processor (202), in conjunction with the machine learning and analytics unit (212), processes the raw incoming data. This step includes collect noise filtering, data cleaning, normalization, temporal alignment, and handling of missing or inconsistent data. Feature extraction is performed to derive meaningful spatial, temporal, and contextual attributes, including environmental trends, anomaly indicators, and correlation patterns across multiple data modalities.
[0066] At step (306), the system performs cross-modal data fusion using advanced fusion techniques, such as attention-based weighting mechanisms, to integrate heterogeneous data streams into a unified representation. This enables coherent interpretation of complex disaster scenarios by aligning spatial, temporal, and environmental features from diverse sources. The fused data is then provided to the adaptive AI prediction module for further analysis.
[0067] At step (308), the machine learning and analytics unit (212) applies trained artificial intelligence and machine learning models to analyze the fused data. The system detects anomalies, classifies disaster types, and predicts disaster onset, progression, and potential impact zones. In certain embodiments, a spatio-temporal digital twin simulation is generated to model the evolution of disaster events over time, enabling predictive visualization and scenario-based forecasting. The system may also assign confidence scores to predictions to support reliability assessment.
[0068] At step (310), based on the predictive analysis, the system generates real-time situational intelligence outputs and alerts through the alert and decision support unit (214). Alerts are categorized based on severity levels, confidence scores, and predicted impact, and may include advisory notifications, warning alerts, or emergency escalation messages. The system also generates dynamic hazard maps, risk assessments, and actionable recommendations for disaster response and mitigation.
[0069] At step (312), the transceiver (206) transmits alerts and associated situational intelligence data through the communication network (106) to one or more endpoints, including user devices (104), emergency response authorities, and control centers. In certain embodiments, geospatial intelligence, predictive reports, and annotated hazard visualizations are shared to facilitate rapid decision-making and coordinated response actions.
[0070] At step (314), the system incorporates feedback and adaptive learning mechanisms. Real-time data, user inputs, and outcome observations are used to evaluate prediction performance, detect model drift, and update model parameters. Selective retraining or model refinement may be triggered based on predefined conditions, ensuring continuous improvement in accuracy and responsiveness.
[0071] In one embodiment, the AI models learn from a mix of real-time data about the environment, historical disaster datasets, and made-up situations. The present invention employs sophisticated training methodologies, including weakly supervised learning and generative modeling, to address the diverse and often incomplete nature of disaster-related data. The system trains predictive models using data that is only partially labeled, features that have been derived, and event-level indicators, rather than only fully labeled datasets. Generative AI can be used to fill in missing data and come up with disaster scenarios. This can make models more accurate and reliable when the outcome is not clear.
[0072] Additionally, specialized optimization methods during training to make sure that the model always works and is reliable. These could be loss functions that check that the predicted outputs match known patterns in the environment, that multi-modal features are in line with each other, and that model predictions stay within realistic ranges of how disasters behave. These methods let you make accurate predictions without needing a lot of labeled data, and they also make it easier to use what you've learned in different kinds of disasters.
[0073] The process may then terminate or loop back to step (302) for more monitoring and prediction. This cyclical operation keeps everyone aware of what's going on, lets them quickly find out about disasters, and lets them quickly share useful information. This makes people better prepared for disasters, speeds up their response, and lowers the risk.
[0074] In an exemplary operation, a system that uses an adaptive multi-modal artificial intelligence (AI) engine (100) to predict disasters in real time and gather information about the situation. The system has a distributed data acquisition framework and a processing module. The processing module has a processor, memory, transceiver, input/output unit, multi-modal data interfaces, a machine learning and analytics unit, and an alert and decision support unit. The system also has a centralized and/or cloud-based server that can store, process, and share information about disasters over a secure network. In one version, the processing module is always getting and sending different types of data from satellite images, UAV systems, IoT sensors, and real-time communication streams. The processor and analytics unit look for important features, combine data from different sources, and sort environmental conditions to find problems and predict disasters like floods, wildfires, earthquakes, or industrial accidents. Based on the results of the predictions, the system sends out alerts that are aware of the situation, from advisory notifications to emergency escalations. It also sends geospatial intelligence, hazard maps, and predictive insights to user devices and emergency response authorities so they can act quickly.
[0075] In an embodiment, the processor is set up to get environmental and contextual data from different data acquisition interfaces in different ways and do preprocessing tasks like filtering out noise, normalizing the data, synchronizing the time, and extracting features. Extracted features can be spatial patterns from satellite images, temporal trends from sensor data, signs of anomalies, and connections between different environmental factors. The processor uses AI and machine learning models that have been trained to figure out what kind of disaster it is, how bad it is, and how it will get worse. The processor uses the analysis to send out alerts and situational intelligence outputs in real time, such as risk classifications and advice on what to do. The processor also lets the transceiver send alerts and predictions to user devices, emergency contacts, or control centers. This makes it easier to make quick choices and work together to deal with disasters.
[0076] In another embodiment, the system uses a cloud-based server to make predictions more accurate and more useful. The distributed sensing infrastructure sends data in many different forms to the server all the time. After that, the server runs advanced AI and ML models that were trained on big datasets of disasters and the environment. The server checks the predictions made by edge or local processing units, adjusts the model parameters, and changes the thresholds on the fly as the environment changes. The system can also make combined situational intelligence reports that show hazard maps, predicted impact zones, severity levels, and ways to lessen the damage. These reports can be sent automatically to disaster management authorities and response teams to help them plan, allocate resources, and take action in real time. Combining edge and cloud intelligence makes systems more stable, speeds up response times, and makes sure that important disaster events are found and reported correctly.
[0077] In yet another embodiment, the system keeps improving its predictive models by using adaptive and privacy-preserving learning methods, such as federated learning. Edge devices or local processing units use data that is specific to a region or environment to update models without sending raw data to central servers. Instead, a central aggregation system gets updates or model parameters that don't say who made them. After that, this system sends the improved global model back out to the network. This system makes predictions more accurate in different parts of the world while keeping data private and lowering the cost of communication. In real life, the system watches the weather all the time and looks for signs of a disaster, like rising water levels or strange thermal patterns. The AI engine looks at the data, figures out how a disaster will unfold, and sends out the right alerts, like advisory warnings for moderate risks and emergency alerts for serious situations. This lets people avoid disasters, get people out of harm's way on time, and respond quickly to emergencies.
[0078] Figure 4 is a block diagram illustrating an advanced multi-modal disaster prediction and situational intelligence module (400) integrated within the adaptive multi-modal artificial intelligence (AI) engine (100), in accordance with an embodiment of the present disclosure. The module (400) is configured to process heterogeneous data acquired from multiple sources, perform cross-modal fusion and predictive analytics using artificial intelligence and machine learning techniques, and generate dynamic situational intelligence outputs including hazard predictions, risk classifications, and alert signals.
[0079] The module (400) comprises a data acquisition interface unit (402), configured to receive raw multi-modal data from satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and real-time communication streams. The incoming data may include structured environmental parameters, unstructured image and video data, and contextual textual information. The acquired data is then transmitted to a preprocessing unit (404), which performs noise filtering, data cleaning, normalization, temporal synchronization, and handling of missing or inconsistent data, thereby ensuring that reliable and high-quality data is available for downstream processing.
[0080] A feature extraction and transformation unit (406) is configured to derive relevant spatial, temporal, and environmental features from the preprocessed data. These features may include geospatial patterns, anomaly indicators, temporal trends, environmental correlations, and contextual signals indicative of potential disaster events. The extracted features are further processed to generate structured representations suitable for predictive modeling and cross-modal analysis.
[0081] The module (400) further includes a cross-modal fusion and analytics unit (408), configured to integrate features from heterogeneous data sources using advanced fusion mechanisms, such as attention-based weighting or context-aware correlation techniques. The fusion unit generates a unified representation of the monitored environment, enabling coherent interpretation of complex and dynamic disaster scenarios. The fused data is then provided to an adaptive AI prediction engine, which applies trained machine learning and deep learning models to detect anomalies, classify disaster types, and predict disaster onset, progression, and potential impact zones.
[0082] In an embodiment, the module (400) incorporates a spatio-temporal digital twin simulation unit (410), configured to create a dynamic virtual model of the physical environment. This unit simulates disaster propagation over time, including spread of floods, wildfire expansion, or seismic impact zones, enabling predictive visualization and scenario-based analysis. Additionally, a generative modeling unit (412) may be included to reconstruct missing data, simulate hypothetical disaster conditions, and enhance robustness of predictions under uncertain or incomplete data conditions.
[0083] Outputs from the predictive and simulation units are processed by a decision and intelligence engine (414), which assigns severity levels, computes confidence scores, and determines appropriate response actions. Based on predefined thresholds and contextual factors, the decision engine categorizes events into advisory, warning, or emergency levels and generates corresponding alerts. A situational intelligence generator (416) compiles outputs such as time-evolving hazard maps, risk zone delineations, predictive reports, and recommended mitigation strategies, which may be stored locally or transmitted via the communication network to relevant stakeholders.
[0084] The module (400) brings together multi-source data acquisition, advanced feature extraction, intelligent data fusion, predictive analytics, simulation, and decision-making into a single framework. This makes it possible to accurately predict disasters in real time and create actionable situational intelligence, which greatly improves early warning systems, operational efficiency, and disaster response effectiveness.
[0085] In one embodiment, the adaptive multi-modal artificial intelligence (AI) engine (100) trains its machine learning and analytics module offline before it is used. A variety of large-scale disaster-related datasets are used for this. These datasets could include satellite images, data from UAVs, readings from IoT sensors, records of past disasters, and real-time communication data for different types of disasters, such as floods, wildfires, earthquakes, and industrial hazards. Training directly with fully labeled, high-resolution multi-modal datasets is often hard because the data is sparse, noisy, has missing values, and it's hard to get accurate annotations for disasters that are happening right now. The current invention employs a weakly supervised and hybrid training strategy to address these issues. This means that the AI models learn from a mix of event-level labels (like "flood occurrence" or "fire outbreak"), environmental indicators that come from the environment (like "temperature anomalies," "water level variations," or "seismic patterns"), and large-scale historical or population-level disaster reports. This method reduces the need for extensive labeling while maintaining high prediction accuracy and robustness.
[0086] In another embodiment, the training framework uses advanced optimization techniques and specialized loss functions to make sure that the predictions are correct and follow the laws of physics. A simulation-consistency loss makes sure that the model's outputs match known patterns of how disasters spread. This makes sure that the predictions of how things will change are accurate. A projection-based loss makes sure that high-dimensional fused representations and lower-dimensional environmental indicators, like statistical indices and temporal trends, are all in line with each other. It's easier to get and check these indicators. A distribution alignment mechanism also makes sure that predicted disaster patterns match up with distributions that have already happened. This makes things less confusing when the data is noisy or incomplete. When used together, these methods make model training more stable, predictions more accurate in a wider range of environmental conditions, and real-time disaster predictions more reliable.
[0087] In yet another embodiment, the training process uses semi-supervised learning and multi-modal fusion to make its predictions better. Satellite images, IoT sensors, UAV systems, and communication streams all work together to get extra information from different places and times. Semi-supervised learning techniques let the system use a lot of data that doesn't have labels by making fake labels that are checked against confidence thresholds. This makes the system more flexible and able to grow. You can also use federated learning to make models better in a distributed way. In this case, edge devices or local nodes use data from a specific region to change model parameters without sending raw data to centralized servers. Instead, model updates that don't have any identifying information are put together to make a better global model. This protects people's privacy, follows the law, and makes things work better in different parts of the world.
[0088] The AI/ML model is put into the processing system (100) so that it can make decisions in real time after it has been trained. The machine learning and analytics unit is always looking at incoming multi-modal data and comparing it to learned patterns in the environment and disaster templates. It then makes predictions about when disasters will happen, how they will progress, and how bad they will be. The system uses the predictive outputs to give confidence scores, create situational intelligence, and send out alerts that can be anything from advisory notifications to emergency escalations. Adaptive learning methods like federated updates and feedback integration can help the model get better over time. This makes sure that it stays strong, accurate, and responsive even when things go wrong.
[0089] Figure 5 is a flowchart illustrating an artificial intelligence and machine learning (AI/ML)-based method for multi-modal disaster detection, prediction, and alert generation, in accordance with an embodiment of the present disclosure. The method leverages the adaptive multi-modal artificial intelligence (AI) engine (100) to continuously process heterogeneous data streams and generate real-time situational intelligence for disaster management.
[0090] The method begins at step (502), wherein heterogeneous data is acquired from a plurality of data sources, including satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and real-time communication streams. The acquired data may include geospatial imagery, environmental parameters such as temperature, humidity, pressure, water levels, and seismic activity, as well as contextual information from communication channels relevant to disaster events.
[0091] At step (504), the acquired data is processed by a preprocessing unit to perform operations such as noise filtering, data cleaning, normalization, temporal synchronization, and removal of inconsistencies or artifacts. This step ensures that the data is standardized and suitable for reliable downstream analysis, particularly in scenarios involving incomplete or noisy inputs.
[0092] At step (506), relevant features are extracted from the preprocessed data. These features may include spatial patterns derived from imagery, temporal trends from sensor readings, anomaly indicators, environmental correlations, and contextual signals indicative of potential disaster conditions. The extracted features are transformed into structured representations to facilitate multi-modal analysis.
[0093] At step (508), the extracted features are input into a cross-modal fusion and machine learning module, which integrates heterogeneous data streams into a unified representation using advanced fusion techniques such as attention-based weighting or context-aware correlation. The fused data is then analyzed using trained AI/ML models, including deep neural networks or hybrid architectures, to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones.
[0094] At step (510), a decision engine evaluates the prediction results and assigns severity levels and confidence scores. Disaster events may be categorized into multiple risk levels, such as low-risk, moderate-risk, or critical-risk scenarios, based on predicted impact and probability. The decision engine may also incorporate outputs from a spatio-temporal digital twin simulation module to refine severity assessment and forecast disaster evolution.
[0095] At step (512), an alert and decision support unit generates context-specific alerts and recommendations based on the classification and severity assessment. Alerts may include advisory notifications for early-stage risks, warning alerts for escalating conditions, and emergency alerts for critical disaster events requiring immediate action. The system may also generate dynamic situational intelligence outputs such as hazard maps, risk zones, and predictive analytics.
[0096] At step (514), a reporting module compiles comprehensive situational intelligence reports, including geospatial visualizations, predicted impact zones, event timelines, severity classifications, and recommended mitigation strategies. These reports may be stored locally or transmitted to centralized systems for further analysis and coordination.
[0097] At step (516), the transceiver securely transmits alerts, reports, and situational intelligence outputs to one or more endpoints, including user devices, disaster management authorities, emergency responders, and control centers via the communication network (106). In certain embodiments, the transmitted data is encrypted and stored in cloud-based infrastructure for secure access and real-time collaboration.
[0098] At step (518), the process loops back to step (502) to enable continuous monitoring, prediction, and alert generation. This iterative and real-time operational cycle ensures uninterrupted situational awareness, early detection of disaster events, and timely dissemination of actionable intelligence, thereby enhancing disaster preparedness, response efficiency, and overall risk mitigation.
[0099] In one embodiment of the present invention a method is disclose to predict disasters in real time and gather information about the situation. The adaptive multi-modal artificial intelligence (AI) engine (100) constantly collects several types of data from integrated data sources, like satellite images, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensor networks, and real-time communication streams. The collected data is cleaned up for ignore noise, and which make sure that it is reliable for further analysis. The processed data is used to find spatial, temporal, and environmental features, such as geospatial patterns, anomaly indicators, temporal trends, and environmental correlations. An AI-based machine learning use for look for strange patterns, sorts disaster types, and predicts when disasters will happen and the seviourity. A decision engine looks at the predictive outputs, gives them severity levels data and confidence scores, which sends out alerts from advisory notifications to emergency escalation. Also, situational intelligence outputs like hazard maps, impact assessments, and predictive reports can be made and safely sent to user devices, emergency responders, and control centers so that they can act quickly. This example shows how the disclosed method combines continuous monitoring, smart prediction, and communication based on severity to make disaster management more proactive.
[00100] The present invention employs a combination of sophisticated artificial intelligence (AI) and machine learning (ML) models specifically designed for multi-modal data processing, disaster forecasting, and the generation of situational intelligence. The system uses deep learning architectures like Convolutional Neural Networks (CNNs) to extract spatial features from satellite and UAV images, Represent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to model temporal sequences of sensor data (like seismic signals and weather patterns), and Transformer-based attention models to fuse data from different sources. Graph Neural Networks (GNNs) can also be used to model how disasters spread and how they affect different areas that are connected to each other. Generative AI models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs) can be used to fill in missing data and create possible disaster scenarios.
[00101] In one embodiment, the AI models are trained using a hybrid training framework to supervised like learning labeled historical disaster datasets, annotated satellite imagery, sensor logs, and event records that disaster type, severity, and progression. However,the system also uses weakly supervised learning with event-level labels (like "flood occurrence" or "wildfire outbreak") and derived indicators like "temperature anomalies" or "water-level thresholds." Semi-supervised learning techniques allow the system to use a lot of unlabeled data by making pseudo-labels that are checked against confidence thresholds. This makes the system more scalable and improves model generalization.
[00102] In another embodiment, the training process uses advanced optimization techniques and loss functions that are aware for the domain to make sure that the predictions are physically consistent and reliable. For example, a spatio-temporal consistency makes sure that the predicted evolution of a disaster matches known physical patterns, like how a flood spreads downstream or how a wildfire spreads with the wind. A cross-modal alignment loss makes sure that features from different modalities (like satellite imagery and IoT sensor data) are consistent with each other. A distribution alignment constraint makes sure that predicted outputs stay in line with patterns of disasters that have happened in the past. These mechanisms make the system more stable, make predictions less uncertain, and help the model converge more quickly during training.
[00103] In yet another embodiment, of the present invention, federated learning and edge–cloud collaborative training is used to make models better all the time without putting data privacy at risk. Edge devices like UAVs or IoT nodes do localized training with data from their own regions, and only anonymized model updates are sent to a central server. The server collects updates from many nodes to improve a global model, which sent back to edge devices. This method lets the system work in different parts of the world, collect information about disasters that happen in areas, and stay in line with data privacy and security rules.
[00104] In a further embodiment, the system combines a spatio-temporal digital twin model with reinforcement or simulation-based learning. The AI models are trained on both past data and simulated disaster scenarios. The digital twin constantly makes fake environments so that disasters can happen and how to respond. Reinforcement learning methods can be used to improve decision-making policies, like planning for evacuation and allocating resources, based on what is tends to happen. With real-world data training, generative simulation, and adaptive learning all working together, the system can make very accurate, context-aware, and proactive predictions about disasters and provide situational intelligence.
[00105] In one or more embodiments of the invention, an escalation protocol is provided to send alerts that take into account the severity and expected effects of disaster events. The system sends monitor alerts when it sees normal or low-risk environmental conditions, but it doesn't take any action. When the system sees moderate-risk situations, like slow changes in the environment that could lead to danger, it sends out advisory alerts that tell people to take precautions. When the system detects a major disaster, like a fast-rising flood, a spreading wildfire, or seismic activity, it sends out emergency alerts right away. These alerts go to authorities, emergency contacts, and disaster response agencies, along with geospatial intelligence and predicted impact zones. The escalation protocol runs all the time, so the system starts monitoring again after each alert to keep up with changing conditions. This tiered response system makes operations more efficient, cuts down on false alarms, and helps with quick and effective disaster response.
[00106] The present invention provides numerous technical benefits compared to conventional disaster management systems. The combination of different types of data sources allows for full monitoring of environmental conditions over time and space. The system can find complex patterns and subtle signs of disaster events that other systems might miss thanks to advanced preprocessing, feature extraction, and cross-modal fusion. Additionally, employing artificial intelligence and machine learning models trained on varied datasets facilitates precise forecasting of disaster initiation, development, and consequences, thus minimizing false positives and overlooked detections. The addition of spatio-temporal digital twin simulation makes predictive ability even better by allowing for dynamic modeling of how disasters change over time. Also, using severity-based alerts and decision support systems makes sure that useful and relevant information is delivered, which makes responses more effective and resources better used.
[00107] Taking into account the benefits and technical progress mentioned above, the claimed system and method offer a real technical solution to the problems of predicting disasters in real time and being aware of what is going on. The disclosed method allows for accurate integration of multi-modal data, smart prediction of complex environmental events, adaptive learning, and real-time alert generation. This makes disaster management systems work better and makes them more responsive in changing environments.
[00108] The present invention provides a pragmatic and technically sound resolution to the issue of postponed or erroneous disaster detection through the integration of diverse data collection, sophisticated AI-driven analytics, and instantaneous communication systems. Some of the technical features are cross-modal data fusion, generative reconstruction of incomplete data, spatio-temporal simulation of disaster propagation, and alert generation based on confidence. The system also has secure data transmission and works with decision support systems, which lets for quick triage, coordination, and intervention. The invention creates an integrated platform for predicting and managing disasters before they happen by putting these parts together.
[00109] Furthermore, the claimed invention is a unique mix of technologies and methods that solves problems that have been around for a long time in disaster monitoring and prediction. Individual components like remote sensing, sensor networks, and AI-based analytics are known, but putting them all together into a single adaptive system that can do real-time multi-modal analysis, predictive simulation, and context-aware alerting is a big step forward. The system stands out from other methods because it can link different data sources, model how disasters change over time, and change predictions on the fly. This makes it more useful in real life.
[00110] A person with average skill in the field will understand that the systems, modules, and methods described here are just examples and not the only ones. To meet certain operational needs or environmental conditions, variations, modifications, and alternative implementations may be used without going outside the scope of the present invention.
[00111] The steps and system modules that are shown can also be rearranged, left out, or added to. The embodiments can be put into hardware, software, firmware, middleware, microcode, or any combination of these. The claims are meant to cover a wide range of implementations, such as distributed architectures, edge-cloud systems, and hybrid processing environments.
[00112] The present invention has been delineated with respect to specific embodiments; however, it will be recognized by practitioners in the field that numerous modifications, substitutions, and equivalents may be implemented without deviating from the essence and scope of the invention. The present inventions not limited to the specific embodiments described herein but is intended to encompass all modifications and equivalents within the scope of the appended claims.
, Claims:WE CLAIM
1. A system (100) for real-time disaster prediction and situational intelligence, the system (100) comprising:
a data acquisition module configured to collect heterogeneous data from a plurality of sources including satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and real-time communication streams;
a preprocessing module configured to perform noise filtering, normalization, temporal alignment, and missing data handling on the collected data;
a cross-modal data fusion engine configured to integrate the heterogeneous data using an attention-based weighting mechanism to generate a unified representation;
a spatio-temporal digital twin module configured to generate a dynamic virtual representation of a monitored environment and simulate disaster propagation over time;
an adaptive artificial intelligence (AI) prediction module configured to analyze the fused data to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones;
a generative reconstruction module configured to reconstruct incomplete or missing data and simulate hypothetical disaster scenarios;
a situational intelligence generation module configured to generate outputs including hazard maps, risk classifications, and predictive alerts;
a feedback and adaptive learning module configured to detect model drift and perform selective retraining based on real-time and historical data; and
a user interface and decision support module configured to present actionable intelligence to users.

2. The system as claimed in claim 1, wherein the data acquisition module is configured to collect real-time environmental parameters including temperature, humidity, atmospheric pressure, seismic activity, water levels, and wind patterns.

3. The system as claimed in claim 1, wherein the cross-modal data fusion engine utilizes transformer-based attention mechanisms to dynamically prioritize data sources based on contextual relevance.

4. The system as claimed in claim 1, wherein the adaptive AI prediction module comprises one or more models selected from convolutional neural networks (CNNs), represent neural networks (RNNs), long short-term memory (LSTM) networks, transformer models, and graph neural networks (GNNs).

5. The system as claimed in claim 1, wherein the feedback and adaptive learning module is configured to implement federated learning by aggregating anonymized model updates from distributed edge devices.

6. A method for real-time disaster prediction and situational intelligence, the method comprising:
collecting heterogeneous data from a plurality of sources including satellite imagery, UAV systems, IoT sensors, and communication streams;
preprocessing the collected data by performing noise filtering, normalization, temporal alignment, and missing data handling;
extracting features and performing cross-modal data fusion using attention-based mechanisms to generate a unified representation;
generating a spatio-temporal digital twin of a monitored environment and simulating disaster propagation;
applying adaptive artificial intelligence models to detect anomalies, classify disaster types, and predict disaster onset, progression, and impact zones;
reconstructing incomplete data and simulating scenarios using generative models;
generating situational intelligence outputs including hazard maps, risk classifications, and alerts;
assigning severity levels and confidence scores to predictions; and
transmitting alerts and intelligence outputs to one or more users or systems.

7. The method as claimed in claim 6, wherein the step of feature extraction includes deriving spatial, temporal, and environmental features including anomaly indicators and correlation patterns across multiple data modalities.

8. The method as claimed in claim 6, wherein the step of applying adaptive artificial intelligence models includes using deep learning architectures for spatial analysis, temporal prediction, and graph-based propagation modeling.

9. The method as claimed in claim 6, further comprising generating dynamic hazard maps and time-evolving risk zones based on predicted disaster progression.

10. The method as claimed in claim 6, further comprising updating the artificial intelligence models using feedback-based adaptive learning and federated learning without transmitting raw data to a central server.