MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202611112526 A) filed by Jeetu Rani; N Radhika Amareshwari; Prof. Dr. Sanjay Raosaheb Sawate; Dr. K. Maheswari; Chaitali Mahesh Gunjal; Karishma Mittal; Saurabh Rajaram Wale; Angelin Kiruba P; Amol Dayanand Sutar; Pradnesh Ramesh Padave; Dr. Deepika Jayavel; and Meera Bai C on September 19, 2026, for Machine Learning-Based Intelligent System For Predicting Super El Niño-Induced Water Demand, Agricultural Loss And Economic Impact.
Inventors include Jeetu Rani; N Radhika Amareshwari; Prof. Dr. Sanjay Raosaheb Sawate; Dr. K. Maheswari; Chaitali Mahesh Gunjal; Karishma Mittal; Saurabh Rajaram Wale; Angelin Kiruba P; Amol Dayanand Sutar; Pradnesh Ramesh Padave; Dr. Deepika Jayavel; and Meera Bai C.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: The present invention relates to a Machine Learning-Based Intelligent System for Predicting Super El Niño-Induced Water Demand, Agricultural Loss and Economic Impact. Existing climate monitoring and forecasting systems provide useful information on El Niño conditions, rainfall, temperature, and other environmental changes. However, climate information is often considered separately from the practical effects that may follow, such as increased agricultural water demand, crop losses, and economic exposure. Conventional assessments may also depend on historical averages or individual climate indicators and may not adequately represent differences between regions, crops, water availability, and local economic conditions. The proposed invention addresses these limitations by bringing climate, water, agricultural, and economic information into a single prediction and assessment system. The system generates specific indicators including Super El Niño Intensity Score (SEIS), Water Demand Stress Index (WDSI), Crop Water Deficit Index (CWDI), Agricultural Loss Risk Score (ALRS), Climate Sensitivity Index (CSI), and Economic Impact Exposure Score (EIES). These indicators are processed together with historical and newly available information by a machine learning-based prediction module to estimate water demand, agricultural loss, and related economic impact for selected regions and time periods. The system further evaluates predicted conditions and provides decision-support information for water allocation, irrigation planning, crop planning, drought preparedness, and resource prioritization. A continuous feedback mechanism compares predictions with subsequently observed conditions and updates the assessment. The potential outcome is earlier identification of water stress and agricultural risk, better understanding of regional economic exposure, more informed resource planning, and continuously updated impact assessment during severe El Niño conditions. FIG.1
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