Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to intervention and control system, more specifically, relates to artificial rainfall control system based on machine learning techniques.
BACKGROUND OF THE DISCLOSURE
[0002] The practice of cloud seeding for artificial rainfall is crucial for mitigating drought, supporting agriculture, and managing water resources. However, uncontrolled rainfall can disrupt ecosystems, damage crops, and impact infrastructure, making its regulation essential. Excessive rain can lead to soil erosion, flooding, and unintended consequences in neighbouring regions, raising ethical and environmental concerns. Challenges such as unpredictable cloud behaviour, wind variability, and limited counteragents make it difficult to precisely control rainfall. To address these issues, advanced weather modelling and the development of effective systems are necessary. Additionally, implementing strict regulatory frameworks can ensure that artificial rainfall operations are conducted responsibly without harming natural weather patterns or ecosystems.
[0003] Traditional inventions face several limitations, making precise regulation challenging. Unpredictable cloud behaviour remains a major issue, as atmospheric conditions can change rapidly, making it difficult to control where and how much rain falls. Wind and air currents can carry seeded clouds away from the intended area, causing unintended rainfall in neighbouring regions. Many traditional systems cannot differentiate between natural and artificial weather modifications, making unauthorized activities difficult to identify and counteract. They are not accurate enough regarding rainfall moderation after seeding is carried out. Moreover, legal and ethical concerns surrounding artificial weather modification restrict experimentation and large-scale deployment. These limitations highlight the need for improved predictive models, better countermeasures, and stricter regulatory oversight to ensure responsible and effective artificial rainfall control.
[0004] The present invention addresses the limitations of the prior art by developing an artificial rainfall control system. The proposed invention addresses the problem of unauthorized or unintended artificial downfall by using a detector- driven, predictive, and visionary intervention system. It monitors and limits artificial rainfall at the time of its formation by disturbing the clouds with the help of advanced technologies. Further, the present invention predicts and prevents artificial rainfall before it causes adverse effects. In addition, Autonomous operations of the present invention reduce the need for manual intervention and can scale across large geographical regions. It provides real-time data to regulatory bodies, enabling enforcement of weather modification and ensuring transparency. Moreover, the present invention combines detection, prediction and prevention into a unified system, eliminating the need for fragmented or disjoined technologies.
[0005] Thus, in light of the above-stated discussion, there exists a need for an artificial rainfall control system.
SUMMARY OF THE DISCLOSURE
[0006] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0007] According to illustrative embodiments, the present disclosure focuses on an artificial rainfall control system which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0008] An objective of the present disclosure is to develop an artificial rainfall control system to predicts and prevents artificial rainfall before it happens.
[0009] Another objective of the present disclosure is to combine detection, prediction, and intervention in a single system, streamlining operations and ensuring a cohesive approach to artificial rainfall prevention.
[0010] Another objective of the present disclosure is to create a system to ensure precise, targeted interventions, eliminating the need for manual intervention and reducing human error.
[0011] Another objective of the present disclosure is to develop a system which is adaptable to various environments and scalable for deployment globally, from urban areas to remote regions.
[0012] Another objective of the present disclosure is to create a system that provide real-time data that supports compliance, transparency, and accountability in weather modification activities.
[0013] Yet another objective of the present disclosure is to accurately identifies artificial weather modification activities and enables proactive action based on predictive insights.
[0014] In light of the above, in one aspect of the present disclosure, an artificial rainfall control system is disclosed herein. The system comprises a sensing unit allocated at various areas of pre-defined locations, wherein the sensing unit further comprises a plurality of sensors configured to measure meteorological parameters and cloud seedling patterns. The system includes a plurality of drones equipped with counteractive agents and configured to fly into rain-forming clouds to control artificial rainfall. The system also includes a communication network configured to enable communication within the system. The system also includes a processing unit connected to the sensing unit and the plurality of drones via the communication network and configured to process real-time data and active responses, wherein the processing unit further comprises an input module configured to receive real-time data from the sensing unit, a pre-processing module configured to remove noise and irrelevant data from the received data, a feature extraction module configured to extract features from the pre-processed data for further analysis, an anomalies detection module configured to analyse the extracted features to identify anomalies in artificial rainfall using machine learning, a vaticination module configured to evaluate the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions, an activation module configured to activate and emplace the plurality of drones to target the affected regions, an implementation module configured to deploy the pre-defined amount of counteractive agents from the plurality of drones to neutralise artificial downfall processes, a monitoring module configured to monitor the post-intervention to ensure successful neutralisation of artificial rainfall, an output module configured to transmit and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention. The system also includes a user device connected to the processing unit the communication network and configured to receive and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through a user interface.
[0015] In one embodiment, the system further comprises cloud database configured to store real-time information related to the parameters and risk associated with the artificial rainfall.
[0016] In one embodiment, the counteractive agents include hygroscopic particles, ice nucleation inhibitors and cloud dissipation agents.
[0017] In one embodiment, the processing unit further comprises training and testing module configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset.
[0018] In one embodiment, the anomalies detection module matches the extracted features against the pre-saved data stored in the cloud database and flag deviations that exceeds pre-defined thresholds.
[0019] In one embodiment, the processing unit further comprises an alert generation module configured to generate timely alerts based on the identified anomalies and the extent of artificial rainfall.
[0020] In one embodiment, the alert generation module configured to send alert to the user device of the concerned authority to ensure safety and timely decision-making.
[0021] In one embodiment, the user device is access by the concerned authority to monitor, control and response to the artificial rainfall operations in real time.
[0022] In one embodiment, the plurality of drones coordinate conditioning securely with the concerned authority via the communication network.
[0023] In light of the above, in one aspect of the present disclosure, a method for controlling artificial rainfall is disclosed herein. The method comprising measuring meteorological parameters and cloud seedling via a plurality of sensors of a sensing unit. The method includes enabling communication within the system via a communication network. The method also includes receiving real-time data from the sensing unit via an input module. The method further includes removing noise and irrelevant data from the received data via a pre-processing module. Furthermore, the method includes extracting features from the pre-processed data for further analysis via a feature extraction module. in addition, the method includes analysing the extracted features to identify anomalies in artificial rainfall using machine learning via an anomalies detection module. Also, the method includes evaluating the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions via a vaticination module. Moreover, the method includes activating and emplacing the plurality of drones to target the affected regions via an activation module. Additionally, the method includes deploying the pre-defined amount of counteractive agents from the plurality of drones to neutralise artificial downfall processes via an implementation module. The method also includes monitoring the post-intervention to ensure successful neutralisation of artificial rainfall via a monitoring module. Further, the method includes transmitting and displaying the vaticination associated with the identified anomalies, the generated alerts, and post-intervention via an output module. At Last, the method includes receiving and displaying the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through a user interface via a user device.
[0024] These and other advantages will be apparent from the present application of the embodiments described herein.
[0025] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0026] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0028] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0029] FIG. 1 illustrates a block diagram of an artificial rainfall control system, in accordance with an embodiment of the present disclosure; and
[0030] FIG. 2 illustrates a flow-chart of a method, outlining the sequential steps for controlling artificial rainfall, in accordance with an embodiment of the present disclosure.
[0031] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0032] The artificial rainfall control system is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0033] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure.
[0034] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0035] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0036] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0037] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0038] Referring now to FIG. 1 to FIG. 10 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an artificial rainfall control system 100, in accordance with an embodiment of the present disclosure.
[0039] The system 100 comprises a sensing unit 102, a plurality of drones 104, a communication network 106, a processing unit 108 which further comprises input module 118, the pre-processing module 120, the training and testing module 122, the feature extraction 124, the anomalies detection module 126, the vaticination module 128, the alert generation module 130, the activation module 132, the implementation module 134, the monitoring module 136 and the output module 138. The system also includes a user device 110.
[0040] The sensing unit 102 allocated at various areas of pre-defined locations, wherein the sensing unit 102 further comprises the plurality of sensors 114 configured to measure meteorological parameters and cloud seedling patterns. The pre-defined locations of the sensing units 102 are strategically placed in areas that are able to provide the most accurate and comprehensive data for cloud formation, seeding effectiveness, and overall weather conditions.
[0041] In one embodiment of the present invention, the sensing unit 102 comprising the plurality of sensors 114 may include ground-based sensors, aircraft integrated sensors, satellite, seeding aerosol concentration sensors, LIDAR (Light Discovery and ranging) and Airborne chemical detectors.
[0042] In one embodiment of the present invention, the ground-based sensors are used to measure meteorological parameters including temperature, humidity, wind speed and direction, barometric pressure and rainfall rates. The aircraft integrated sensors are used to measure wind patterns at high altitudes, cloud temperature and moisture content. Satellites detects cloud optical properties, such as cloud thickness and reflectivity, which provide insight into cloud formation and seeding effectiveness. The seeding aerosol concentration sensors to monitor the concentration of the seeding agent in the atmosphere and track its dispersion over the targeted area. The LIDAR is used to track cloud movements after seeding operations. The Airborne chemical detectors for detecting artificial pesticides.
[0043] The plurality of drones 104 equipped with counteractive agents and configured to fly into rain-forming clouds to control artificial rainfall. The plurality of drones 104 Drones is fly directly into rain-forming clouds and release counteragents exactly where needed to disrupt or neutralize artificial downfall processes.
[0044] In one embodiment of the present invention, the plurality of drones 104 disperse counteragents such as hygroscopic patches to dissolve seeded capitals and heat dispersers to destabilize seeded shadows. They are powered by renewable energy for sustained deployment.
[0045] In one embodiment of the present invention, the counteractive agents include hygroscopic particles, ice nucleation inhibitors and cloud dissipation agents.
[0046] In one embodiment of the present invention, the plurality of drones 104 coordinate conditioning securely with the concerned authority via the communication network 106.
[0001] The communication network 106 configured to enable communication within the system 100. The communication network 106 transmits data from the sensing unit 102 to the processing unit 108 and further to other components in the system 100. Further, it ensures compliance with nonsupervisory fabrics by furnishing transparent reports and real-time monitoring data to authorized agencies.
[0002] In one embodiment of the present invention, the communication network 106 may include wired and wireless networks.
[0003] In preferred embodiment of the present invention, the communication network 106 includes wireless network to enable effective communication within the system 100.
[0004] The processing unit 108 connected to the sensing unit 102 and the plurality of drones 104 via the communication network 106 and configured to process real-time data and active responses, wherein the processing unit 108 further comprises the input module 118, the pre-processing module 120, the training and testing module 122, the feature extraction 124, the anomalies detection module 126, the vaticination module 128, the alert generation module 130, the activation module 132, the implementation module 134, the monitoring module 136 and the output module 138.
[0005] The input module 118 configured to receive real-time data from the sensing unit 102.
[0006] The pre-processing module 120 configured to remove noise and irrelevant data from the received data. Additionally, data pre-processing involves resizing images, converting them to grayscale, and applying histogram equalization to enhance the contrast of facial features, ultimately improving recognition accuracy. This process improves data quality, reduces computational costs, and leads to better outcomes.
[0007] In one embodiment of the present invention, the processing unit 108 further comprises training and testing module 122 configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset. This module 122 trains the learning model using the training dataset, allowing the model to learn from the data by fine-tuning pre-trained weights. After the training, the testing dataset is used to evaluate its performance, ensuring that the model can generalize effectively to new, unseen data. This process ensures the model’s accuracy and reliability when applied to real-world scenarios.
[0008] The feature extraction module 124 configured to extract features from the pre-processed data for further analysis. This module 124 extract features from the pre-processed data using pre-trained convolutional models.
[0009] In one embodiment of the present invention, the pre-trained convolutional models may include AlexNet, VGG, ResNet, MobileNet, EfficientNet and other similar architectures.
[0010] The anomalies detection module 126 configured to analyse the extracted features to identify anomalies in artificial rainfall using machine learning.
[0011] In one embodiment of the present invention, the anomalies such as increased levels of seeding agents, unnatural cloud growth, abnormal temperature fluctuations, unusual wind patterns, irregular precipitation patterns and other several patterns.
[0012] In one embodiment of the present invention, the system 100 further comprises cloud database 112 configured to store real-time information related to the parameters and risk associated with the artificial rainfall. The cloud database 112 regularly updates real-time data to ensure accurate and timely information for decision-making and analysis in the cloud seeding process.
[0013] In one embodiment of the present invention, the anomalies detection module 126 matches the extracted features against the pre-saved data stored in the cloud database 112 and flag deviations that exceeds pre-defined thresholds. For instance, seeding agent levels, cloud growth patterns, precipitation rates and temperature exceed the threshold value, it is flagged as a potential anomaly, indicating cloud dissipation.
[0014] The vaticination module 128 configured to evaluate the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions. It also forecasts the amount, duration, and geographical extent of the rainfall, helping to optimize the cloud seeding process and reduce risks like flooding or cloud instability. This enables more accurate decision-making in artificial rainfall operations.
[0015] In one embodiment of the present invention, the processing unit 108 further comprises an alert generation module 130 configured to generate timely alerts based on the identified anomalies and the extent of artificial rainfall.
[0016] In one embodiment of the present invention, the alert generation module 130 configured to send alert to the user device 110 of the concerned authority to ensure safety and timely decision-making. Sending alerts to the user device 110 allows for continuous engagement and control, keeping the concerned authority informed about anomalies or critical changes in the environmental parameters.
[0017] The activation module 132 configured to activate and emplace the plurality of drones 104 to target the affected regions. This module 132 activates the plurality of drones 104 equipped with the counteractive agents and deployed to the area of concern. This includes sending commands to control unit of each of the plurality of drone 104, which controls its flight path, altitude, and operations. The control unit calculates the optimal flight path to the target area, considering factors such as airspace restrictions, weather conditions, and battery life. The navigation unit in each of the plurality of drones 104 is used to guide it to the designated area.
[0018] The implementation module 134 configured to deploy the pre-defined amount of counteractive agents from the plurality of drones 104 to neutralise artificial downfall processes. Upon arrival at the target area, each of the plurality of drones 104 is ready to deploy the counteractive agents to neutralize seeded nuclei or destabilize artificially induced clouds.
[0019] The monitoring module 136 configured to monitor the post-intervention to ensure successful neutralisation of artificial rainfall. It monitors the drone's progress and performance using real-time data. Cloud dynamics are also observed post-intervention via the monitoring module 136 to ensure successful neutralization. If any anomalies or issues arise, such as changes in weather conditions or flight difficulties, the system 100 is able to send corrective commands to the plurality of drones 104.
[0020] The output module 138 configured to transmit and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention.
[0021] The user device 110 connected to the processing unit 108 via the communication network 106 and configured to receive and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through a user interface 116.
[0001] In one embodiment of the present invention, the user device 110 is access by the concerned authority to monitor, control and response to the artificial rainfall operations in real time.
[0002] FIG. 2 illustrates a flow-chart of a method 200, outlining the sequential steps for controlling artificial rainfall, in accordance with an embodiment of the present disclosure.
[0003] At step 202, the system 100 measures meteorological parameters and cloud seedling via a plurality of sensors 114 of the sensing unit 102.
[0004] At step 204, the system 100 enables communication within the system 100 via the communication network 106.
[0005] At step 206, the system 100 receives real-time data via the input module 118 from the sensing unit 102.
[0006] At step 208, the system 100 removes noise and irrelevant data from the received data via the pre-processing module 120.
[0007] At step 210, the system 100 extract features from the pre-processed data via the feature extraction module 124 for further analysis.
[0008] At step 212, the system 100 analyses the extracted features via the anomalies detection module 126 to identify anomalies in artificial rainfall using machine learning.
[0009] At step 214, the system 100 evaluates the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions via the vaticination module 128.
[0010] At step 216, the system 100 activates and emplaces the plurality of drones 104 to target the affected regions via the activation module 132.
[0011] At step 218, the system 100 deploys the pre-defined amount of counteractive agents from the plurality of drones 104 to neutralise artificial downfall processes via the implementation module 134.
[0012] At step 220, the system 100 monitors the post-intervention to ensure successful neutralisation of artificial rainfall via the monitoring module 136.
[0013] At step 222, the system 100 transmits and displays the vaticination associated with the identified anomalies, the generated alerts, and post-intervention via the output module 138.
[0014] At step 224, the system 100 receives and displays the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through the user interface 116 via the user device 110.
[0015] The best mode of operation of the present invention, the system 100 utilizes the sensing unit 102, which contains the plurality of sensors 114 placed at predefined locations to measure environmental parameters associated with artificial rainfall. the plurality of sensors 114 is used to assess the area that may be affected by the artificial rainfall, helping navigate the plurality of drones 104 carrying counteractive agents. The input module 118 of the processing unit receives real-time data from the sensing unit, which is then pre-processed by the pre-processing module 120. This step involves resizing, converting to grayscale, and removing noise or irrelevant data to improve data quality. The cleaned data is fed into the training and testing module 122, which trains and tests the system 100. The anomalies detection module 126 analyses the processed data, identifying unusual patterns and behaviours by comparing the extracted features to pre-saved data in the cloud database 112 using machine learning. These detected patterns are then assessed by the vaticination module 128, which predicts the likelihood and extent of artificial rainfall in the affected area. The alert generation module 130 sends notifications to the user device 110 which is accessed by concerned authority, prompting them to take necessary action. Once the possibility of artificial rainfall is determined, the activation module 132 activates the plurality of drones 104 and are deployed to the target area, and with assistance from the implementation module 134, the plurality of drones 104 are deployed to release counteractive agents to disrupt cloud formation and prevent precipitation. Finally, the monitoring module 136 tracks the intervention's effectiveness and post intervention, while the output module 138 generates an output showing detected anomalies, alerts, and post-intervention status, which is transmitted to the user interface 116 of the user device 110 for review and further action.
[0016] The present invention comprises a system 100 designed to predict and prevent artificial rainfall before it leads to adverse effects. It integrates detection, prediction, and prevention into a cohesive system that ultimately eliminates the need for separate, disconnected technologies. The system 100 utilizes advance models to anticipate artificial rainfall events in advance, allowing for proactive interventions rather than waiting for reactive responses. Further, the system 100 employs airborne units such as drone equipped with counteractive agents to directly disrupt the artificial rainfall process. The artificial rainfall control system 100 is highly adaptable, capable of being deployed in diverse environments and scalable for global use, ranging from urban centres to remote areas.
[0017] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0018] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0019] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0020] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0021] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An artificial rainfall control system (100), the system (100) comprising:
a sensing unit (102) allocated at various areas of pre-defined locations, wherein the sensing unit (102) further comprises a plurality of sensors (114) configured to measure meteorological parameters and cloud seedling patterns;
a plurality of drones (104) equipped with counteractive agents and configured to fly into rain-forming clouds to control artificial rainfall;
a communication network (106) configured to enable communication within the system (100);
a processing unit (108) connected to the sensing unit (102) and the plurality of drones (104) via the communication network (106) and configured to process real-time data and active responses, wherein the processing unit (108) further comprises:
an input module (118) configured to receive real-time data from the sensing unit (102);
a pre-processing module (120) configured to remove noise and irrelevant data from the received data;
a feature extraction module (124) configured to extract features from the pre-processed data for further analysis;
an anomalies detection module (126) configured to analyse the extracted features to identify anomalies in artificial rainfall using machine learning;
a vaticination module (128) configured to evaluate the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions;
an activation module (132) configured to activate and emplace the plurality of drones (104) to target the affected regions;
an implementation module (134) configured to deploy the pre-defined amount of counteractive agents from the plurality of drones (104) to neutralise artificial downfall processes;
a monitoring module (136) configured to monitor the post-intervention to ensure successful neutralisation of artificial rainfall;
an output module (138) configured to transmit and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention; and
a user device (110) connected to the processing unit (108) via the communication network (106) and configured to receive and display the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through a user interface (116).
2. The system (100) as claimed on claim 1, wherein the system (100) further comprises cloud database (112) configured to store real-time information related to the parameters and risk associated with the artificial rainfall.
3. The system (100) as claimed in claim 1, wherein the counteractive agents include hygroscopic particles, ice nucleation inhibitors and cloud dissipation agents.
4. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises training and testing module (122) configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset.
5. The system (100) as claimed in claim 1, wherein the anomalies detection module (126) matches the extracted features against the pre-saved data stored in the cloud database (112) and flag deviations that exceeds pre-defined thresholds.
6. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises an alert generation module (130) configured to generate timely alerts based on the identified anomalies and the extent of artificial rainfall.
7. The system (100) as claimed in claim 1, wherein the alert generation module (130) configured to send alert to the user device (110) of the concerned authority to ensure safety and timely decision-making.
8. The system (100) as claimed in claim 1, wherein the user device (110) is access by the concerned authority to monitor, control and response to the artificial rainfall operations in real time.
9. The system (100) as claimed in claim 1, wherein the plurality of drones (104) coordinate conditioning securely with the concerned authority via the communication network (106).
10. A method (200) for controlling artificial rainfall, the method (200) comprising:
measuring meteorological parameters and cloud seedling via a plurality of sensors (114) of a sensing unit (102);
enabling communication within the system (100) via a communication network (106);
receiving real-time data from the sensing unit (102) via an input module (118);
removing noise and irrelevant data from the received data via a pre-processing module (120);
extracting features from the pre-processed data for further analysis via a feature extraction module (124);
analysing the extracted features to identify anomalies in artificial rainfall using machine learning via an anomalies detection module (126);
evaluating the identified anomalies to assess the liability and extent of artificial downfall grounded on current conditions via a vaticination module (128);
activating and emplacing the plurality of drones (104) to target the affected regions via an activation module (132);
deploying the pre-defined amount of counteractive agents from the plurality of drones (104) to neutralise artificial downfall processes via an implementation module (134);
monitoring the post-intervention to ensure successful neutralisation of artificial rainfall via a monitoring module (136);
transmitting and displaying the vaticination associated with the identified anomalies, the generated alerts, and post-intervention via an output module (138); and
receiving and displaying the vaticination associated with the identified anomalies, the generated alerts, and post-intervention through a user interface (116) via a user device (110).