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@article{190578,
        author = {Vaibhav Vijay Nhayade and Pankaj R Patil and Yash Dharamchand Bafna and Prashant Sardar Khandikar and Sarvesh Dinesh Wani},
        title = {Predictive Modeling of Groundwater Resources Using Machine Learning and Spatial Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4425-4431},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190578},
        abstract = {Groundwater serves as a primary source of fresh- water for agriculture, domestic use, and industrial needs across India. However, rapid urbanization, over-extraction, and inconsistent rainfall patterns have led to severe groundwater depletion in various regions. Existing monitoring systems are either too sparse or delayed, limiting their effectiveness for timely decision- making. To address these challenges, this research proposes a scalable, AI-driven framework that integrates machine learning and geospatial techniques for accurate groundwater level prediction.
The system utilizes multi-source datasets including historical well-level records, rainfall statistics, land use patterns, population density, and digital elevation models. An ensemble machine learning approach combining Random Forest, XGBoost, and Support Vector Machines (SVM) is employed for temporal prediction, while spatial interpolation is handled using geospatial techniques such as Inverse Distance Weighting (IDW). Missing data is addressed using imputation techniques and transfer learning is employed for improving prediction accuracy in data- scarce regions.
The model is deployed as an interactive web platform built with React and Flask, allowing users to input geographic locations and retrieve predictive insights, alerts, and downloadable reports. The platform targets both citizens and government authorities, providing personalized forecasts, resource planning tools, and data visualizations. Results from the implementation indicate improved prediction accuracy and practical applicability for sustainable water resource management.},
        keywords = {Groundwater Prediction, Machine Learning, Random Forest, XGBoost, SVM, Geospatial Analysis, Sustain- ability},
        month = {January},
        }