AI-Driven Spatial Data Analysis of Groundwater Level and Gravimetric Data in Roorkee Region, India

Abstract. Excessive consumption of groundwater can lead to a significant imbalance between groundwater recharge rates and water demand. This disparity underscores the importance of accurately estimating future groundwater storage to ensure global water and food security, in line with sustainable development goals (SDGs) related to clean water and sanitation and sustainable cities and communities. However, traditional methods face challenges in predicting groundwater storage due to their inherent complexity. To address this gap and align with SDGs, this study aims to develop a regression-based machine learning model for spatially varying groundwater level prediction. The primary goal is to improve local water resource management and encourage responsible water usage. The study evaluates the use of K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM), XG Boost and Polynomial regression models, using two groups of input parameters. The results show that the XG-Boost model establishes a strong relationship between input and output parameters. The developed KNN model can be reliably used for local groundwater level prediction and can also contribute to sustainable urban development, ultimately aligning with the SDGs.