MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202611057148 A) filed by Bhupendra Upadhyay; Garv Yadav; Shivangi Tyagi; Ashima Arya; Neha Bhatia; Manvi Khatri; Umang Kant; Anjali Chauhan; and Mayank Tyagi on May 05, 2026, for Automated Machine Learning-Based Prediction Of Ground Water For Agriculture And Industrial Applications.
Inventors include Bhupendra Upadhyay; Garv Yadav; Shivangi Tyagi; Ashima Arya; Neha Bhatia; Manvi Khatri; Umang Kant; Anjali Chauhan; and Mayank Tyagi.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The present invention discloses an automated machine learning system (100) for location-specific groundwater safety classification suitable for agricultural and industrial applications. The system integrates a data acquisition module (110), a data preprocessing engine (120), a feature engineering unit (130), a multi-model machine learning classification engine (140), a GPS-based spatial prediction module (150), an evaluation and model selection unit (160), and a decision support and output interface (170). Physicochemical groundwater parameters together with geographic location data expressed as GPS coordinates are processed through supervised classification algorithms including Logistic Regression, K-Nearest Neighbors, Ridge Classifier, Random Forest, Gradient Boosting, and XGBoost to assign each sampled location a safety label of safe, moderate, or unsafe. The XGBoost classifier achieves superior performance with an accuracy of 99.33 percent. Integration of GPS coordinates enables spatially explicit safety mapping, making the system applicable for real-time policy-level water resource management.
Disclaimer: Curated by HT Syndication.
