Machine Learning for Smart Water Management: Predicting Consumption and Leak Detection

Abstract

With the increasing demand for sustainable water management, IoT and machine learning are being leveraged to predict water consumption patterns and detect leaks. This paper explores ML models such as decision trees, gradient boosting, and recurrent neural networks (RNNs) for water conservation. It discusses challenges like data accuracy, sensor failures, and system integration. Case studies highlight ML-driven water management solutions, showcasing their impact on reducing water waste, improving distribution efficiency, and promoting sustainability.