MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085382 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for Ai-Enabled Nature-Based Digital Twin System For Autonomous Water Source Identification, Recharge Mapping And Sustainability Management In Mountain Ecosystems.
Inventors include Dr. Anandakumar H; Dr. R. Babitha Lincy; Ms. Minu Balakrishnan; and Mr. S. Aravind.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Mountain ecosystems serve as critical sources of freshwater, yet identifying sustainable water sources and suitable groundwater recharge zones remains challenging due to rugged terrain, climate variability, limited accessibility, and fragmented environmental data. Conventional watershed assessment methods rely on periodic field surveys and static geospatial analysis, making them inadequate for continuous monitoring and timely decision-making. This study proposes an AI-Enabled Nature-Based Digital Twin System for Autonomous Water Source Identification, Recharge Mapping, and Sustainability Management in Mountain Ecosystems. The proposed framework integrates distributed IoT sensors, UAV-based thermal and multispectral imaging, LiDAR data, satellite remote sensing, GIS, and hydrological datasets within a dynamic Digital Twin platform. Artificial Intelligence techniques are employed to analyze terrain characteristics, soil moisture, rainfall, groundwater fluctuations, vegetation health, and hydrological patterns to autonomously identify potential water sources, delineate groundwater recharge zones, and assess drought vulnerability. Edge computing enables real-time data processing, reducing communication latency and ensuring continuous operation in remote mountainous regions with limited connectivity. The Digital Twin continuously synchronizes field observations with virtual watershed models to support real-time visualization, scenario simulation, predictive analysis, and resource planning. The system further generates sustainability recommendations for watershed restoration, rainwater harvesting, recharge enhancement, and ecosystem conservation while providing timely alerts and decision support through web and mobile applications. By combining AI-driven analytics with nature-based solutions and Digital Twin technology, the proposed framework offers an intelligent, scalable, and proactive approach for sustainable mountain water resource management, improving water security, ecosystem resilience, and long- term environmental sustainability
Disclaimer: Curated by HT Syndication.
