Predictive Modeling for Water Anomaly Detection Using Machine Learning

Water scarcity along with poor management of water sources is turning to be a matter of global concern, and this calls for an intelligent monitoring system. This paper introduces a new predictive modeling technique for the anomalous detection in water consumption utilizing AI and machine learning for optimally optimizing and consuming water. The system combines a Smart Water Meter with a web-based UI that uses ML and deep learning algorithms to detect anomalies, predict overflow, and optimize energy and water consumption. This proposed model, in using real-time data and predictive analytics, enables more effective decision-making on the part of both consumers and utility providers regarding the distribution and conservation of water. Its capacity to identify unusual consumption patterns and waste makes this a landmark step forward in smart water management.