MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202611074618 A) filed by Abhilashi University; Dr. Ashwani Kumar; Dr. Lalit Kumar; and Diwaker on June 16, 2026, for Satellite And Ai Integrated Early Warning System For Cloudburst And Excess Rainfall Events.
Inventors include Dr. Ashwani Kumar; Dr. Lalit Kumar; and Diwaker.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: [505] The invention pertains to an integrated early warning system that combines satellite observation technology with artificial intelligence algorithms to detect instances of excessive rainfall and cloudbursts, particularly in hilly and geographically vulnerable regions. The system continuously processes real-time data from satellite imagery, radar precipitation signals, IoT sensor networks, and meteorological parameters through an AI-powered analytics engine to generate probabilistic forecasts of cloudburst likelihood, rainfall intensity, and geographic risk levels. By employing deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models for pattern recognition and predictive modelling, the system achieves high-accuracy detection while minimizing false alarm rates. [510] Existing early warning systems exhibit critical deficiencies in their capacity to simultaneously achieve low false positive alert rates, high sensitivity detection of sudden convective rainfall, timely and targeted alert dissemination, and adaptive model improvement over time. The present invention addresses all such deficiencies through a unified multi-source data fusion framework that enables real-time, localized, and actionable cloudburst warnings with operational end-to-end alert latency of less than 5 to 10 minutes. [515] The alert dissemination module is configured to deliver multi-channel early warnings through mobile application notifications, network-based communication systems, device-based signalling mechanisms including sirens and LED beacons, and SMS-based broadcast systems to pertinent authorities and at-risk communities. The module supports location-specific targeting, tiered alarm levels, and redundancy via parallel communication pathways to guarantee prompt and dependable delivery of vital alerts even in high-risk or low-connectivity environments. [520] The present invention describes a comprehensive Satellite and AI Integrated Early Warning System incorporating satellite communication links, high-precision ground- based rainfall sensors, microcontroller-based edge processing units, AI-driven nowcasting engines, geographic risk mapping modules, and multi-channel alert dissemination platforms within a unified autonomous hydrometeorological monitoring and early warning architecture. Validation of the system demonstrates superior performance in detection accuracy, warning lead time, and operational reliability compared to conventional threshold-based and single-sensor early warning methodologies. Keywords: Hydrometeorological Monitoring; Artificial Intelligence (AI); Early Warning System; Cloudburst Detection; Satellite Communication; Data Fusion; Deep Learning; Nowcasting
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