MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641080284 A) filed by Koneru Vijaya Lakshmi; Institute For Social And Economic Change Isec; and Pruthvi M. S. on June 30, 2026, for A Machine Learning Model For Assessing Climate Change Vulnerability.
Inventors include Pruthvi M. S.; Sunil Nautiyal; and Sudhanshu Singh.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention involves the development of an advanced, AI-powered framework designed to assess and predict climate change vulnerability across diverse geographical regions and industrial sectors. To address the limitations of static, manual evaluation models, the proposed system integrates a high-dimensional data pipeline that ingests real-time and historical data from satellite imagery, IoT-based environmental sensors, meteorological records, and socio-economic indicators. The core of the invention utilizes a multi-layered machine learning architecture comprising Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest algorithms to generate precise vulnerability indices. A key feature of the system is the incorporation of Explainable AI (XAI) modules, such as SHAP (Shapley Additive Explanations), to provide transparent, interpretable insights for decision-makers. The architecture is designed for cloud-based deployment, featuring a continuous learning mechanism that retrains models in response to evolving climate patterns and real-world feedback loops. The system provides a scalable, high-granularity decision support tool that categorises regions into risk tiers, enabling governments, businesses, and disaster management authorities to implement proactive adaptation strategies, optimise resource allocation, and enhance long-term climate resilience. The design demonstrates a robust methodology for shifting from reactive to anticipatory climate risk management through automated, prescriptive analytics.
Disclaimer: Curated by HT Syndication.
