WATER SCARCITY PREDICTION USING MACHINE LEARNING

Abstract

Water scarcity has become a critical global challenge due to climate change, population growth, urbanization, and unsustainable resource management. This study proposes a water scarcity prediction framework that uses data-driven techniques to forecast future water availability and demand. The system integrates historical rainfall data, temperature patterns, groundwater levels, reservoir storage, and population statistics to identify trends and predict potential shortages. Machine learning models such as regression, time-series forecasting, and classification algorithms are applied to analyze environmental and socio-economic factors influencing water resources. The proposed approach enables early warning of water stress conditions, helping governments and local authorities make informed decisions regarding water distribution, conservation strategies, and infrastructure planning. Experimental results indicate that predictive analytics can significantly improve the accuracy of water shortage forecasts compared to traditional methods. The model supports sustainable water resource management by promoting proactive planning, efficient utilization, and risk mitigation, ultimately contributing to long-term environmental stability and improved community resilience against water crises.