1. A system (100) for drainage management to prevent micro-floods in an urban drainage network, the system (100) comprising: a drainage monitoring unit (102), comprising drainage mounted monitoring nodes (104a-104n) installed at predetermined drainage locations, adapted to sense multi-parameter drainage data, wherein the multi-parameter drainage data is selected from a water level, a flow rate, a blockage indicator, a vibration pattern, rainfall data, and microclimatic conditions, or a combination thereof; an input unit (106) adapted to receive multi-parameter drainage data from the drainage mounted monitoring nodes (104a-104n) installed at predetermined drainage locations, wherein the multi-parameter drainage data is selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof; an input conditioning unit (108), operatively coupled to the drainage monitoring unit (102) and the input unit (106), adapted to preprocess the received multi-parameter drainage data by filtering noise, normalizing signal parameters, and structuring the multi-parameter drainage data for predictive analysis; an edge processing unit (110), embedded within the drainage mounted monitoring nodes (104a-104n), adapted to process the sensed multi-parameter drainage data and execute a machine learning based predictive model; a processor (112) operatively coupled to the input conditioning unit (108), characterized in that the processor (112) is configured to: deploy drainage mounted monitoring nodes (104a-104n) at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof; continuously sense multi-parameter drainage data; process the sensed multi-parameter drainage data at the edge processing unit (110) embedded within each monitoring node; execute a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit (110); generate a flood-risk score and determine whether the flood-risk score exceeds a predefined threshold; activate a local actuator (118), upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation; transmit a predictive flood data, a multi-parameter drainage status data, the flood-risk score, and an actuation log, or a combination thereof to an analytics platform (120); and generate geo-tagged alerts, flood heatmaps, and maintenance recommendations for municipal authorities through an alert unit (122).

2. The system (100) as claimed in claim 1, comprising an output unit (114) operatively coupled to the processor (112) and adapted to generate control signals upon the flood-risk score exceeding the predefined threshold and transmit predictive flood data to the analytics platform (120) for centralized visualization and urban drainage planning.

3. The system (100) as claimed in claim 1, comprising an actuation interface unit (116) adapted to receive the control signals from the output unit (114) and activate the local actuator (118) upon exceeding the intervention threshold comprising the micro-flusher, the flow-regulating valve, the pump, the diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation.

4. The system (100) as claimed in claim 1, wherein the alert unit (122) is adapted to generate geo-tagged flood-risk alerts, predictive notifications, and actuation logs to the analytics platform (120).

5. The system (100) as claimed in claim 1, wherein the machine learning based predictive model executed by the edge processing unit (110) comprises a recurrent neural network trained on historical rainfall-runoff-drainage datasets and adapted to perform predictive inference without requiring continuous cloud connectivity.

6. The system (100) as claimed in claim 1, wherein the drainage monitoring unit (102) adapted to detect early-stage blockage conditions using fused ultrasonic backscatter analysis and vibration-spectrum anomaly detection to identify debris accumulation and siltation prior to overflow formation.

7. The system (100) as claimed in claim 1, comprising a power supply unit (124) adapted to provide electrical power to the drainage monitoring unit (102), the edge processing unit (110), and the actuation interface unit (116).

8. A method (300) for drainage management to prevent micro-floods in an urban drainage network, the method (300) is characterized by steps of: deploying drainage mounted monitoring nodes (104a-104n) at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof; continuously sensing multi-parameter drainage data; processing the sensed multi-parameter drainage data at an edge processing unit (110) embedded within each monitoring node; executing a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit (110); generating a flood-risk score and determining whether the flood-risk score exceeds a predefined threshold; and activating a local actuator (118), upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation.

9. The method (300) as claimed in claim 8, comprising a step of transmitting a predictive flood data, a multi-parameter drainage status data, the flood-risk score, and an actuation log, or a combination thereof to an analytics platform (120).

10. The method (300) as claimed in claim 8, comprising a step of generating geo-tagged alerts, flood heatmaps, and maintenance recommendations for municipal authorities through an alert unit (122). Date: March 09, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:
BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to climate drainage management and particularly to system and method for drainage management to prevent micro-floods.
Description of Related Art
[002] Rapid urbanization and climate variability cause frequent and intense rainfall events in metropolitan regions. Conventional drainage networks face excess stormwater load that exceeds design capacity, that results in localized water accumulation, street-level micro-floods, traffic disruption, infrastructure damage, wastewater contamination, and public health risks.
[003] Low-lying zones, manholes, and culverts experience overflow due to sediment deposition, debris accumulation, reverse flow, and inadequate hydraulic response. Municipal authorities often lack real-time visibility into drainage performance across distributed urban locations.
[004] Existing drainage management practices rely on manual inspection, periodic maintenance schedules, and threshold-based water level alarms. Utilities deploy water-level sensors, flow meters, or telemetry-based sewer monitoring systems to detect overflow conditions. Some municipal systems integrate remote dashboards or supervisory control platforms for centralized observation of lift stations and drainage channels. In certain deployments, alerts prompt field teams to inspect and clear blockages or adjust pump operations.
[005] Despite such measures, present solutions primarily detect overflow after water accumulation occurs. Many systems depend on centralized communication infrastructure and continuous connectivity. Blockage detection remains limited to basic level sensing without debris characterization or predictive risk estimation.
[006] Manual intervention leads to response delay, and threshold-based alerts lack analytical forecasting of rainfall-runoff interaction. Consequently, current drainage networks remain reactive rather than proactive in addressing micro-flood risk across urban environments.
[007] There is thus a need for an improved and advanced system and method for drainage management to prevent micro-floods that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[008] Embodiments in accordance with the present invention provide a system for drainage management to prevent micro-floods. The system comprising a drainage monitoring unit, comprising drainage mounted monitoring nodes installed at predetermined drainage locations, adapted to sense multi-parameter drainage data. The multi-parameter drainage data is selected from a water level, a flow rate, a blockage indicator, a vibration pattern, rainfall data, and microclimatic conditions, or a combination thereof. The system further comprising an input unit adapted to receive multi-parameter drainage data from the drainage mounted monitoring nodes installed at predetermined drainage locations. The multi-parameter drainage data is selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof. The system further comprising an input conditioning unit, operatively coupled to the drainage monitoring unit and the input unit, adapted to preprocess the received multi-parameter drainage data by filtering noise, normalizing signal parameters, and structuring the multi-parameter drainage data for predictive analysis. The system further comprising an edge processing unit, embedded within the drainage mounted monitoring nodes, adapted to process the sense multi-parameter drainage data and execute a machine learning based predictive model. The system further comprising a processor operatively coupled to the input conditioning unit. The processor is configured to deploy drainage mounted monitoring nodes at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof; continuously sense multi-parameter drainage data; process the sensed multi-parameter drainage data at the edge processing unit embedded within each monitoring node; execute a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit; generate a flood-risk score and determine whether the flood-risk score exceeds a predefined threshold; activate a local actuator, upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation; transmit a predictive flood data, a multi-parameter drainage status data, the flood-risk score, and an actuation log, or a combination thereof to an analytics platform; and generate geo-tagged alerts, flood heatmaps, and maintenance recommendations for municipal authorities through an alert unit.
[009] Embodiments in accordance with the present invention further provide a method for drainage management to prevent micro-floods. The method comprising steps of deploying drainage mounted monitoring nodes at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof; continuously sensing multi-parameter drainage data; processing the sensed multi-parameter drainage data at an edge processing unit embedded within each monitoring node; executing a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit; generating a flood-risk score and determining whether the flood-risk score exceeds a predefined threshold; and activating a local actuator, upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation.
[0010] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for drainage management to prevent micro-floods.
[0011] Next, embodiments of the present application may provide a system that enables real-time, multi-parameter monitoring of drainage conditions at distributed micro-drainage points for early identification of abnormal flow and blockage conditions.
[0012] Next, embodiments of the present application may provide a system that performs edge-based predictive analytics to estimate micro-flood risk prior to visible water accumulation Thus, facilitating proactive intervention.
[0013] Next, embodiments of the present application may provide a system that autonomously actuates localized flow-control mechanisms, including valves, pumps, and micro-flushers, to regulate stormwater discharge without manual intervention.
[0014] Next, embodiments of the present application may provide a system that supports scalable deployment across multiple drainage zones through coordinated data aggregation and synchronized actuation logic within a distributed network framework.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1A illustrates a block diagram of a system for drainage management to prevent micro-floods, according to an embodiment of the present invention;
[0016] FIG. 1B illustrates an exemplary embodiment of the system for drainage management to prevent micro-floods, according to an embodiment of the present invention;
[0017] FIG. 2 illustrates a block diagram of a processing unit of the system for drainage management to prevent micro-floods, according to an embodiment of the present invention; and
[0018] FIG. 3 depicts a flowchart of a method for drainage management to prevent micro-floods, according to an embodiment of the present invention.
DETAILED DESCRIPTION
[0019] FIG. 1A illustrates a block diagram of a system 100 for drainage management to prevent micro-floods, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to function as an integrated predictive and responsive drainage control framework capable of performing continuous monitoring, intelligent risk evaluation, autonomous intervention, and centralized supervisory reporting.
[0020] In operation, the system 100 may be adapted to continuously acquire multi-parameter drainage information from distributed drainage locations across a defined geographic region. The acquired information may include hydrological, structural, environmental, and rainfall-related parameters. The system 100 may be configured to condition and analyse the acquired information to identify evolving drainage anomalies and potential flood-forming patterns before visible water accumulation occurs.
[0021] The system 100 may be adapted to execute predictive analytics to determine a probabilistic flood-risk state associated with specific drainage points. The predictive analytics may evaluate temporal flow variations, rainfall-runoff relationships, obstruction indicators, and drainage capacity utilization trends. Based on the predictive evaluation, the system 100 may compute a risk indicator representing the likelihood of localized flooding within a predefined time horizon.
[0022] Upon determining that the computed risk indicator exceeds a predefined intervention criterion, the system 100 may be adapted to initiate automated corrective action. The corrective action may include regulation of flow, diversion of excess stormwater, clearing of early-stage obstructions, or controlled discharge to prevent water accumulation. The automated response may occur without manual inspection or centralized command dependency.
[0023] Simultaneously, the system 100 may be configured to transmit predictive outcomes, drainage performance status, and intervention logs to a supervisory analytics environment. The supervisory environment may be adapted to aggregate distributed drainage information, generate spatial risk visualizations, produce flood heatmaps, and support municipal decision-making.
[0024] In an embodiment, the system 100 may implement a hybrid operational structure that features localized predictive evaluation that may further enable low-latency response, while aggregated data analysis enables long-term drainage optimization and climate-resilient planning. The system 100 may further be adapted to dynamically adjust predictive thresholds based on rainfall intensity trends, seasonal variations, and historical drainage behaviour patterns.
[0025] The system 100 may operate continuously at defined monitoring intervals to provide proactive micro-flood mitigation. Thus, transforming conventional reactive drainage systems into predictive and autonomous urban flood-prevention networks.
[0026] In an embodiment of the present invention, the system 100 may be robust, scalable, climate-responsive, and adaptive, capable of intelligently conditioning multi-parameter drainage inputs, dynamically managing predictive flood-risk computation and delivering reliable outputs suitable for automated actuation, visualization, and alert generation with minimal latency. In an embodiment of the present invention, predictive flood-risk computation, and delivering reliable outputs suitable for automated actuation, visualization, and alert generation with minimal latency.
[0027] In an embodiment of the present invention, the system 100 may implement a hybrid edge-cloud architecture wherein primary predictive inference may be executed locally at an edge processing unit 110, and secondary analytical aggregation, climate forecasting integration, and long-term trend analysis may be performed at an analytics platform 120. The hybrid architecture may enable low-latency autonomous actuation while supporting centralized supervisory intelligence and strategic urban drainage planning.
[0028] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise a drainage monitoring unit 102, drainage mounted monitoring nodes 104a-104n (herein referred individually to as the drainage mounting monitoring node 104 and plurally to as the drainage mounting monitoring nodes 104), an input unit 106, an input conditioning unit 108, the edge processing unit 110, a processor 112, an output unit 114, an actuation interface unit 116, a local actuator 118, the analytics platform 120, an alert unit 122, and a power supply unit 124. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0029] In an embodiment of the present invention, the drainage monitoring unit 102 may be adapted to detect early-stage blockage conditions using fused ultrasonic backscatter analysis and vibration-spectrum anomaly detection. The fusion of ultrasonic and vibration data may enable identification of debris accumulation, sediment deposition, or structural obstruction within the drainage pathway prior to overflow formation.
[0030] In an embodiment of the present invention, the drainage monitoring unit 102 may comprise drainage mounted monitoring nodes 104 installed at predetermined drainage locations. The predetermined drainage locations may be, but not limited to, manholes, culverts, stormwater drains, low-lying areas, roadside drainage channels, underground pipelines, and other drainage infrastructure components. Embodiments of the present invention are intended to include or otherwise cover any type of drainage deployment environment, including urban, semi-urban, industrial, residential, or mixed-use regions.
[0031] The drainage monitoring unit 102 may be, but not limited to, a distributed sensing assembly, an integrated hydrological monitoring framework, a multi-parameter detection platform, a drainage surveillance subsystem, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the drainage monitoring unit 102, including known, related art, and/or later developed technologies.
[0032] The drainage mounted monitoring nodes 104 may be adapted to sense multi-parameter drainage data. The multi-parameter drainage data may be, but not limited to, a water level, a flow rate, a blockage indicator, a vibration pattern, rainfall data, and microclimatic conditions, and so forth. The drainage mounted monitoring nodes 104 may include, but not limited to, ultrasonic level sensors, flow sensors, vibration sensors, rainfall sensors, environmental sensors, and related sensing technologies. Embodiments of the present invention are intended to include or otherwise cover any type of drainage sensing technologies, including known, related art, and/or later developed technologies. The drainage mounted monitoring nodes 104 may be adapted to sense multi-parameter drainage data continuously or periodically depending on the predetermined drainage locations.
[0033] In an embodiment of the present invention, the drainage mounted monitoring nodes 104 may be configured as modular and retrofittable units adapted for installation within existing drainage infrastructure without requiring structural modification, civil reconstruction, or network redesign. The drainage mounted monitoring nodes 104 may be adapted to configure node-specific parameters based on existing drainage dimensions, hydraulic capacity, and environmental constraints to enable rapid and low-cost installation across legacy drainage networks.
[0034] In an embodiment of the present invention, the drainage mounted monitoring nodes 104 may be adapted to locally buffer multi-parameter drainage data and predictive outputs within onboard memory when network connectivity is unavailable. Upon restoration of communication, the buffered data may be synchronized with the analytics platform 120. This configuration enables uninterrupted predictive monitoring and resilience in intermittent connectivity environments.
[0035] In an embodiment of the present invention, the drainage mounted monitoring nodes 104 may comprise a waterproof and corrosion-resistant housing structure adapted for installation within manholes, culverts, stormwater drains, and underground drainage pipelines. The housing structure may include an ingress-protected enclosure, vibration-dampening mounts, and sediment-resistant protective grilles. The housing structure may be adapted to withstand high humidity, submersion conditions, debris impact, and temperature fluctuations commonly encountered in urban drainage environments.
[0036] The drainage mounted monitoring nodes 104 may be, but not limited to, embedded drainage sensors, IoT-enabled monitoring modules, waterproof sensing nodes, retrofittable drain-installed devices, smart drainage probes, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the drainage mounted monitoring nodes 104, including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the input unit 106 may be adapted to receive multi-parameter drainage data from the drainage mounted monitoring nodes 104 installed at predetermined drainage locations and transmit the data to the input conditioning unit 108. In an embodiment of the present invention, the drainage mounted monitoring nodes 104 and the input unit 106 may comprise a wireless communication module adapted to support long-range and low-power communication technologies including LoRaWAN, NB-IoT, LTE-M, cellular communication, Wi-Fi, or equivalent communication protocols. The wireless communication module may be configured to transmit multi-parameter drainage data, predictive outputs, and actuation logs to the analytics platform 120. The wireless communication module may further support encrypted data transmission, adaptive bandwidth allocation, and fallback communication modes to ensure reliable data exchange within distributed drainage environments. The input unit 106 may be, but not limited to, a data acquisition interface, a communication gateway, a signal receiving module, a telemetry interface subsystem, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the input unit 106, including known, related art, and/or later developed technologies.
[0038] The input conditioning unit 108 may be operatively coupled to the drainage monitoring unit 102 and the input unit 106. The input conditioning unit 108 may be adapted to preprocess the received drainage data for predictive analysis. The preprocessing of the received multi-parameter drainage data may include, but not limited to, noise filtering, signal normalization, signal smoothing, temporal alignment, data structuring, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of preprocessing of the received multi-parameter drainage data, including known, related art, and/or later developed technologies. The input conditioning unit 108 may be, but not limited to, a signal preprocessing module, a data normalization engine, a filtering subsystem, a temporal alignment processor, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the input conditioning unit 108, including known, related art, and/or later developed technologies.
[0039] In an embodiment of the present invention, the edge processing unit 110 may be embedded within the drainage mounted monitoring nodes 104. The edge processing unit 110 may be adapted to process the sensed multi-parameter drainage data and execute a machine learning based predictive model. The machine learning based predictive model may analyse drainage behaviour to detect an abnormal flow behaviour, early blockage conditions, a rainfall-runoff correlation, and a micro-flood risk conditions, and so forth. The predictive model may include, but not limited to, supervised learning models, neural networks, recurrent neural networks, statistical learning techniques, anomaly detection algorithms, or related predictive analytics techniques. Embodiments of the present invention are intended to include or otherwise cover any type of predictive modelling techniques, including known, related art, and/or later developed technologies.
[0040] In an embodiment of the present invention, the machine learning based predictive model executed by the edge processing unit 110 may comprise a recurrent neural network trained on historical rainfall-runoff-drainage datasets. The recurrent neural network may be adapted to model temporal dependencies in drainage behaviour and perform predictive inference locally at the drainage mounted monitoring nodes 104 without requiring continuous cloud connectivity. This configuration enables autonomous predictive evaluation at distributed drainage locations.
[0041] In an embodiment of the present invention, the drainage mounted monitoring nodes 104 and the edge processing unit 110 may be adapted to support over-the-air firmware updates and machine learning model parameter updates. The processor 112 may be configured to securely transmit updated predictive model weights, firmware patches, threshold configurations, and security protocols to the drainage mounted monitoring nodes 104 via the wireless communication module. The over-the-air update mechanism may enable continuous improvement of predictive accuracy and adaptive system performance without requiring physical access to the deployed nodes.
[0042] The edge processing unit 110 may be, but not limited to, an embedded inference processor, a microcontroller-based analytics engine, a low-power computing module, a localized predictive computing subsystem, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the edge processing unit 110, including known, related art, and/or later developed technologies.
[0043] The processor 112 may be operatively coupled to the input conditioning unit 108 and a memory storing executable instructions. The processor 112 may be configured to deploy the drainage mounted monitoring nodes 104 at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, and so forth. The processor 112 may continuously sense multi-parameter drainage data. The multi-parameter drainage data may be, but not limited to, the water level, the flow rate, the blockage indicators, the vibration patterns, the rainfall data, and the microclimatic condition, and so forth. The processor 112 may process the sensed multi-parameter drainage data at the edge processing unit 110 embedded within each monitoring node. The processor 112 may execute the machine learning based predictive model via the edge processing unit 110.
[0044] The processor 112 may generate a flood-risk score based on processed multi-parameter drainage data and may determine whether the flood-risk score satisfies or exceeds a predefined threshold. The processor 112 may repeatedly evaluate flood-risk conditions at predefined intervals to enable near real-time predictive assessment of drainage status. The processor 112 may be, but not limited to, a central processing unit, a microprocessor, a programmable logic controller, a system-on-chip architecture, a distributed computing controller, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 112, including known, related art, and/or later developed technologies. The processor 112 may further be explained in detail in conjunction with FIG. 2.
[0045] In an embodiment of the present invention, the output unit 114 may be adapted to generate control signals upon the flood-risk score exceeding the predefined threshold. The output unit 114 may further transmit predictive flood data, drainage status information, predictive scores and actuation logs to the analytics platform 120. The analytics platform 120 may provide visualization, monitoring, data storage, or system integration functionalities. The output unit 114 may be, but not limited to, a control signal generation module, a transmission interface, a digital output subsystem, a communication output gateway, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the output unit 114, including known, related art, and/or later developed technologies.
[0046] In an embodiment of the present invention, the actuation interface unit 116 may be adapted to receive control signals from the output unit 114. The actuation interface unit 116 may be adapted to activate the local actuator 118 upon determination that the flood-risk score exceeds the predefined threshold. The actuation interface unit 116 may be, but not limited to, a signal conversion interface, a motor driver module, a hydraulic control interface, an actuator command subsystem, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the actuation interface unit 116, including known, related art, and/or later developed technologies.
[0047] In an embodiment of the present invention, the local actuator 118 may be adapted to regulate hydraulic conditions within a drainage pathway in response to a control signal generated by the processor 112. The local actuator 118 may be configured to perform controlled flow modulation, obstruction removal, stormwater diversion, or discharge regulation to prevent water accumulation prior to overflow formation. The local actuator 118 may comprise an electromechanical actuation assembly including a motor-driven valve mechanism, a micro-flushing nozzle system, a compact pump assembly, a diversion gate mechanism, or a combination thereof. The local actuator 118 may be adapted to operate under submerged, high-humidity, and debris-prone environmental conditions within urban drainage infrastructure.
[0048] In an embodiment of the present invention, the local actuator 118 may comprise a motorized flow-regulating valve configured to adjust an effective cross-sectional flow area within a drainage channel. The motorized flow-regulating valve may be adapted to partially or fully open or close in response to predicted micro-flood risk conditions. The motorized flow-regulating valve may include a corrosion-resistant rotary or linear actuation mechanism adapted for long-term installation within stormwater drains and culverts. The motorized flow-regulating valve may be adapted to operate incrementally to enable staged hydraulic regulation rather than binary open-close operation.
[0049] In an embodiment of the present invention, the local actuator 118 may comprise a micro-flushing mechanism configured to generate localized high-velocity water jets for dislodging debris accumulation, sediment deposition, and early-stage obstructions within the drainage pathway. The micro-flushing mechanism may be adapted to activate for a predefined duration upon detection of blockage indicators exceeding a predefined obstruction threshold. The micro-flushing mechanism may be further adapted to operate intermittently in staged pulses to minimize energy consumption while maximizing debris clearance efficiency.
[0050] In an embodiment of the present invention, the local actuator 118 may comprise a compact pump assembly adapted to evacuate excess stormwater from a low-lying drainage point to an adjacent higher-capacity drainage conduit. The compact pump assembly may be configured to activate when the predicted flood-risk score exceeds a predefined threshold. The compact pump assembly may be adapted to operate under variable flow-rate conditions and may include backflow prevention mechanisms to avoid reverse water propagation.
[0051] In an embodiment of the present invention, the local actuator 118 may comprise a diversion gate mechanism configured to redirect excess stormwater toward an alternate drainage pathway. The diversion gate mechanism may be adapted to shift between primary and secondary flow channels based on predictive hydraulic load estimation. The diversion gate mechanism may be further adapted to operate gradually to prevent sudden hydraulic shock within the drainage network.
[0052] In an embodiment of the present invention, the local actuator 118 may comprise a hybrid multi-actuation configuration including a combination of the motorized flow-regulating valve, the micro-flushing mechanism, the compact pump assembly, and the diversion gate mechanism. The hybrid configuration may be adapted to execute sequential or simultaneous actuation strategies depending on the type and severity of predicted drainage anomaly. In an embodiment of the present invention, the local actuator 118 may be adapted to support staged pre-activation logic. The local actuator 118 may be adapted to initialize mechanical drivers, perform self-diagnostics, and partially adjust flow components prior to exceeding the predefined threshold. The staged pre-activation may reduce actuation latency and enable rapid response to sudden flood-risk escalation.
[0053] In an embodiment of the present invention, the local actuator 118 may be enclosed within a sealed, corrosion-resistant housing adapted to withstand continuous water exposure, sediment contact, vibration, and temperature fluctuations. The housing may include ingress-protection sealing, anti-clog protective meshes, and vibration-dampening supports to ensure sustained operational reliability within drainage infrastructure.
[0054] In an embodiment of the present invention, the local actuator 118 may be adapted to operate in a low-power mode when inactive and may draw peak energy only during actuation intervals. The local actuator 118 may be integrated with the power supply unit 124 to enable energy-aware scheduling of actuation events in energy-constrained deployments.
[0055] In an embodiment of the present invention, the local actuator 118 may be adapted to generate post-actuation feedback signals indicative of flow change, obstruction clearance, or hydraulic stabilization. The post-actuation feedback signals may be transmitted to the processor 112 to enable re-evaluation of flood-risk status and confirmation of successful intervention.
[0056] The local actuator 118 may be, but not limited to, a motorized valve, a micro-flushing mechanism, a pump assembly, a diversion gate mechanism, a flow-control actuator, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the local actuator 118, including known, related art, and/or later developed technologies.
[0057] In an embodiment of the present invention, the analytics platform 120 may be adapted to integrate external climate forecasting datasets, weather application programming interfaces, satellite-derived rainfall intensity data, and storm prediction feeds. The analytics platform 120 may be configured to process forecast-based precipitation projections and correlate the forecast data with historical rainfall-runoff-drainage datasets stored in memory associated with the processor 112. The processor 112 may be configured to adjust predefined thresholds dynamically based on forecasted rainfall intensity, storm duration, and predicted runoff accumulation. This configuration enables climate-aware predictive drainage control and anticipatory micro-flood risk mitigation prior to actual rainfall onset.
[0058] In an embodiment of the present invention, the analytics platform 120 may comprise a dashboard interface including a drainage network map module, a flood-risk heatmap visualization module, an actuation log tracking module, and a Drainage Health Index display module. The dashboard interface may be adapted to provide real-time and historical analytical visualization for municipal authorities and urban planners.
[0059] In an embodiment of the present invention, the analytics platform 120 may be adapted to generate long-term drainage performance analytics and climate-resilient urban planning reports based on aggregated multi-year drainage datasets. The processor 112 may analyse recurring blockage patterns, seasonal rainfall-runoff correlations, and infrastructure stress indicators to assist municipal authorities in strategic drainage network expansion and modernization planning.
[0060] In an embodiment of the present invention, the analytics platform 120 and the alert unit 122 may be adapted to integrate with geographic information system platforms and smart city supervisory dashboards. The processor 112 may be configured to map geo-tagged drainage mounted monitoring nodes 104 onto digital urban maps and generate spatial flood-risk heatmaps, drainage performance overlays, and infrastructure vulnerability zones. The integration may enable interoperability with municipal planning systems, disaster management platforms, and urban infrastructure monitoring networks.
[0061] The analytics platform 120 may be, but not limited to, a cloud-based analytics server, a supervisory monitoring system, a centralized data aggregation platform, a smart city dashboard integration system, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the analytics platform 120, including known, related art, and/or later developed technologies.
[0062] In an embodiment of the present invention, the alert unit 122 may be adapted to generate geo-tagged flood-risk alerts, flood heatmaps, and maintenance recommendations for municipal authorities. The alert unit 122 may transmit alerts and analytical summaries to the analytics platform 120 and municipal dashboards for supervisory monitoring. The alert unit 122 may support visual alerts, digital notifications, or data-stream transmission for integration with external systems. Embodiments of the present invention are intended to include or otherwise cover any type of alert outputs, including visual dashboards, notifications, data streams, reports, or control signals.
[0063] The alert unit 122 may be, but not limited to, a notification engine, a digital alert subsystem, a geo-tagged messaging module, a supervisory reporting interface, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the alert unit 122, including known, related art, and/or later developed technologies.
[0064] In an embodiment of the present invention, the power supply unit 124 may be adapted to provide electrical power to the drainage monitoring unit 102, the edge processing unit 110, and the actuation interface unit 116. The power supply unit 124 may include a long-life battery, a photovoltaic micro-panel, a supercapacitor backup, an energy-harvesting subsystem, and so forth. The power supply unit 124 may enable sustained and low-power operation of the system 100 within distributed drainage environments.
[0065] In an embodiment of the present invention, the power supply unit 124 may be integrated at a node level the drainage mounted monitoring nodes 104 and may comprise a photovoltaic micro-panel, a supercapacitor backup unit, a long-life battery, and a low-power microcontroller-based energy management subsystem. The energy management subsystem may be adapted to regulate power consumption of the edge processing unit 110, the wireless communication module, and the actuation interface unit 116 to enable sustained multi-year operation under low-maintenance conditions.
[0066] The power supply unit 124 may be, but not limited to, a long-life battery system, a photovoltaic energy module, a supercapacitor backup subsystem, an energy-harvesting unit, a hybrid power management system, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the power supply unit 124, including known, related art, and/or later developed technologies.
[0067] FIG. 1B illustrates an exemplary embodiment of the system 100, according to an embodiment of the present invention. The embodiment may depict interaction between the drainage monitoring unit 102, the edge processing unit 110, the processor 112, and the actuation interface unit 116 adapted to regulate the local actuator 118 associated with the drainage mounted nodes 104. The embodiment may represent a closed-loop predictive drainage control architecture wherein sensing, predictive evaluation, decision-making, and actuation may occur in a coordinated manner to enable automated micro-flood prevention.
[0068] In operation, the drainage monitoring unit 102 may comprise the drainage mounted nodes 104 may be adapted to sense multi-parameter drainage data including the water level and the flow conditions within the drainage channel. The sensed data may be transmitted to the edge processing unit 110, may execute the machine learning based predictive model to analyse drainage behaviour and determine the abnormal flow or micro-flood risk conditions and so forth. Analytical outputs generated by the edge processing unit 110 may be communicated to the processor 112, may be configured to compute the score and evaluate the score against the predefined threshold.
[0069] Upon determination that the predefined threshold is exceeded, the processor 112 may generate the control signal and transmit the control signal to the actuation interface unit 116. The actuation interface unit 116 may activate the local actuator 118 associated with the drainage mounted nodes 104 to regulate the drainage flow and prevent water accumulation. Thus, enabling automated micro-flood prevention within the urban drainage network.
[0070] FIG. 2 illustrates components of the processor 112 of the system 100, according to an embodiment of the present invention. The processor 112 may comprise a deployment configuration module 200, a predictive analytics module 202, a scoring module 204, an actuation control module 206, and an alert generation module 208.
[0071] In an embodiment of the present invention, the deployment configuration module 200 may be configured to manage installation and operational parameters of the drainage mounted monitoring nodes 104 deployed at predetermined drainage locations. The deployment configuration module 200 may be configured to assign node identifiers, map each of the drainage mounted monitoring nodes 104 to corresponding drainage locations selected from manholes, culverts, stormwater drains, low-lying areas, and so forth. The deployment configuration module 200 may be configured to define sensing intervals, configure predefined thresholds, and establish communication parameters for coordinated operation of the drainage monitoring unit 102. The deployment configuration module 200 may transmit configuration parameters to the drainage mounted monitoring nodes 104 and to the predictive analytics module 202.
[0072] In an embodiment of the present invention, the predictive analytics module 202 may be activated upon receipt of the configuration parameters from the deployment configuration module 200. The predictive analytics module 202 may be configured to receive multi-parameter drainage data from the input conditioning unit 108 and the edge processing unit 110. The multi-parameter drainage data may include the water level, the flow rate, the blockage indicator, the vibration pattern, rainfall data, the microclimatic conditions, and so forth. The predictive analytics module 202 may be configured to execute the machine learning based predictive model to analyse the received multi-parameter drainage data. The predictive analytics module 202 may detect the abnormal flow behaviour, the early blockage conditions, the rainfall-runoff correlation patterns, and the potential micro-flood risk conditions, and so forth..
[0073] In an embodiment of the present invention, the predictive analytics module 202 may be further configured to incorporate drainage network topology modelling and inter-node hydraulic correlation analysis. The processor 112 may analyse spatial interdependencies among multiple drainage mounted monitoring nodes 104 to predict upstream-downstream flow propagation and network-wide flood-risk accumulation. The drainage network modelling may enhance predictive accuracy by incorporating distributed hydrological behaviour rather than isolated node-level evaluation.
[0074] In an embodiment of the present invention, the processor 112 and the predictive analytics module 202 may be configured to estimate a predictive lead-time interval representing a temporal window prior to anticipated micro-flood occurrence. The predictive lead-time interval may be derived from rainfall intensity acceleration patterns, flow-rate gradients, blockage accumulation rate, and historical event comparison metrics. The predictive lead-time interval may be transmitted to the analytics platform 120 to facilitate staged municipal response planning.
[0075] In an embodiment of the present invention, the predictive analytics module 202 may identify deviations in drainage performance based on historical drainage datasets stored in memory. If the predictive analytics module 202 determines that drainage behaviour deviates from predefined operational patterns, then the predictive analytics module 202 may generate analytical outputs indicative of potential micro-flood occurrence and transmit the analytical outputs to the scoring module 204.
[0076] In an embodiment of the present invention, the scoring module 204 may be activated upon receipt of the analytical outputs from the predictive analytics module 202. The scoring module 204 may be configured to compute a composite score representing a likelihood of micro-flood occurrence at a specific drainage location. The scoring module 204 may compare the computed score with a predefined threshold stored in memory. In an exemplary scenario, if the scoring module 204 determines that the computed score exceeds the predefined threshold, then the scoring module 204 may generate a control trigger signal. The scoring module 204 may be further configured to transmit the control trigger signal to the actuation control module 206. In another exemplary scenario, if the scoring module 204 determines that the computed score is less than or equal to the predefined threshold, then the scoring module 204 may be configured to continue receiving analytical outputs from the predictive analytics module 202.
[0077] In an embodiment of the present invention, the scoring module 204 may be further configured to compute a Drainage Health Index representing a composite performance parameter derived from the flood-risk score, blockage probability metrics, flow variability indices, and rainfall-runoff correlation parameters. The Drainage Health Index may be computed using weighted multi-parameter analysis and may be dynamically updated at predefined evaluation intervals. The Drainage Health Index may be transmitted to the analytics platform 120 for visualization, maintenance prioritization, and long-term drainage infrastructure assessment.
[0078] In an embodiment of the present invention, the actuation control module 206 may be activated upon receipt of the control trigger signal from the scoring module 204. The actuation control module 206 may be configured to generate actuator activation signals. The actuator activation signals may be transmitted to the actuation interface unit 116 to activate the local actuator 118 including the micro-flusher, the flow-regulating valve, the pump, the diversion mechanism, and so forth. The activated actuator may regulate the drainage flow to prevent water accumulation within the drainage network. In an embodiment of the present invention, the actuation control module 206 may be configured to implement staged actuator preparation logic prior to full actuation. The actuation control module 206 may pre-condition the local actuator 118 by initializing motor drivers, performing readiness diagnostics, and partially adjusting flow-regulating components before exceeding the predefined threshold. The staged logic may reduce mechanical latency and improve rapid flood-prevention response efficiency. The actuation control module 206 may further be configured to determine an actuation duration and generate a post-actuation verification signal that may enable re-evaluation of drainage parameters by the predictive analytics module 202.
[0079] In an embodiment of the present invention, the alert generation module 208 may be configured to generate geo-tagged flood-risk alerts, predictive notifications, and actuation logs based on the outputs of the scoring module 204 and the actuation control module 206. The alert generation module 208 may format risk-related information for transmission to the analytics platform 120. The alert generation module 208 may enable dashboard visualization, generation of flood heatmaps, and communication of maintenance recommendations to municipal authorities. The alert generation module 208 may be further configured to transmit alert data through the alert unit 122 to external supervisory systems or monitoring dashboards.
[0080] FIG. 3 depicts a flowchart of a method 300 for drainage management to prevent micro-floods, according to an embodiment of the present invention. At step 302, the system 100 may deploy the drainage mounted monitoring nodes 104 at the predetermined drainage points.
[0081] At step 304, the system 100 may continuously sense multi-parameter drainage data. At step 306, the system 100 may process the sensed multi-parameter drainage data at the edge processing unit 110 embedded within each monitoring node.
[0082] At step 308, the system 100 may execute the machine learning based predictive model adapted to detect the abnormal flow behaviour, the early blockage condition, the rainfall-runoff correlation, and the micro-flood risk, and so forth via the edge processing unit 110. At step 310, the system 100 may generate the flood-risk score.
[0083] At step 312, the system 100 may compare the flood-risk score with the predefined threshold. If the flood-risk score exceeds the predefined threshold, then the method 300 may proceed to a step 314. Else, the method 300 may revert to the step 304.
[0084] At step 314, the system 100 may activate the local actuator 118 to regulate the drainage flow and prevent water accumulation.
[0085] At step 316, the system 100 may transmit multi-parameter drainage status data, the flood-risk score, and the actuation logs to the analytics platform 120.
[0086] At step 318, the system 100 may generate the geo-tagged flood-risk alerts, the flood heatmaps, and the maintenance recommendations for the municipal authorities, on the alert unit 122. , Claims:CLAIMS
I/We Claim:
1. A system (100) for drainage management to prevent micro-floods in an urban drainage network, the system (100) comprising:
a drainage monitoring unit (102), comprising drainage mounted monitoring nodes (104a-104n) installed at predetermined drainage locations, adapted to sense multi-parameter drainage data, wherein the multi-parameter drainage data is selected from a water level, a flow rate, a blockage indicator, a vibration pattern, rainfall data, and microclimatic conditions, or a combination thereof;
an input unit (106) adapted to receive multi-parameter drainage data from the drainage mounted monitoring nodes (104a-104n) installed at predetermined drainage locations, wherein the multi-parameter drainage data is selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof;
an input conditioning unit (108), operatively coupled to the drainage monitoring unit (102) and the input unit (106), adapted to preprocess the received multi-parameter drainage data by filtering noise, normalizing signal parameters, and structuring the multi-parameter drainage data for predictive analysis;
an edge processing unit (110), embedded within the drainage mounted monitoring nodes (104a-104n), adapted to process the sensed multi-parameter drainage data and execute a machine learning based predictive model;
a processor (112) operatively coupled to the input conditioning unit (108), characterized in that the processor (112) is configured to:
deploy drainage mounted monitoring nodes (104a-104n) at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof;
continuously sense multi-parameter drainage data;
process the sensed multi-parameter drainage data at the edge processing unit (110) embedded within each monitoring node;
execute a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit (110);
generate a flood-risk score and determine whether the flood-risk score exceeds a predefined threshold;
activate a local actuator (118), upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation;
transmit a predictive flood data, a multi-parameter drainage status data, the flood-risk score, and an actuation log, or a combination thereof to an analytics platform (120); and
generate geo-tagged alerts, flood heatmaps, and maintenance recommendations for municipal authorities through an alert unit (122).
2. The system (100) as claimed in claim 1, comprising an output unit (114) operatively coupled to the processor (112) and adapted to generate control signals upon the flood-risk score exceeding the predefined threshold and transmit predictive flood data to the analytics platform (120) for centralized visualization and urban drainage planning.
3. The system (100) as claimed in claim 1, comprising an actuation interface unit (116) adapted to receive the control signals from the output unit (114) and activate the local actuator (118) upon exceeding the intervention threshold comprising the micro-flusher, the flow-regulating valve, the pump, the diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation.
4. The system (100) as claimed in claim 1, wherein the alert unit (122) is adapted to generate geo-tagged flood-risk alerts, predictive notifications, and actuation logs to the analytics platform (120).
5. The system (100) as claimed in claim 1, wherein the machine learning based predictive model executed by the edge processing unit (110) comprises a recurrent neural network trained on historical rainfall-runoff-drainage datasets and adapted to perform predictive inference without requiring continuous cloud connectivity.
6. The system (100) as claimed in claim 1, wherein the drainage monitoring unit (102) adapted to detect early-stage blockage conditions using fused ultrasonic backscatter analysis and vibration-spectrum anomaly detection to identify debris accumulation and siltation prior to overflow formation.
7. The system (100) as claimed in claim 1, comprising a power supply unit (124) adapted to provide electrical power to the drainage monitoring unit (102), the edge processing unit (110), and the actuation interface unit (116).
8. A method (300) for drainage management to prevent micro-floods in an urban drainage network, the method (300) is characterized by steps of:
deploying drainage mounted monitoring nodes (104a-104n) at predetermined drainage points selected from manholes, culverts, stormwater drains, and low-lying areas, or a combination thereof;
continuously sensing multi-parameter drainage data;
processing the sensed multi-parameter drainage data at an edge processing unit (110) embedded within each monitoring node;
executing a machine learning based predictive model adapted to detect an abnormal flow behaviour, an early blockage condition, a rainfall-runoff correlation, and a micro-flood risk, or a combination thereof via the edge processing unit (110);
generating a flood-risk score and determining whether the flood-risk score exceeds a predefined threshold; and
activating a local actuator (118), upon exceeding the intervention threshold, comprising a micro-flusher, a flow-regulating valve, a pump, a diversion mechanism, or a combination thereof to regulate drainage flow and prevent water accumulation.
9. The method (300) as claimed in claim 8, comprising a step of transmitting a predictive flood data, a multi-parameter drainage status data, the flood-risk score, and an actuation log, or a combination thereof to an analytics platform (120).
10. The method (300) as claimed in claim 8, comprising a step of generating geo-tagged alerts, flood heatmaps, and maintenance recommendations for municipal authorities through an alert unit (122).
Date: March 09, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant