Leveraging Machine Learning to Combat Water Scarcity: Predictive Analysis for Sustainable Water Resource Management
Abstract
Water scarcity is a pressing global issue, particularly in regions vulnerable to climate change. This paper explores the application of machine learning models in predicting water availability and demand patterns, using meteorological, geographical, and consumption data. We propose a forecasting model to aid in sustainable water resource allocation, helping mitigate seasonal shortages and overuse. The research findings suggest that integrating ML-powered predictions into policy-making frameworks can significantly improve water conservation efforts and support sustainable water management practices in both urban and rural settings.
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