AI-Enhanced Digital Twin Platform for Smart Water Distribution: Integrating Machine Learning Models with IoT-Driven Predictive Analytics

Urban water distribution systems are facing unprecedented challenges due to aging in-frastructure, climate change impacts, and increasing demand from growing populations.This paper presents WaterTwin-AI, an innovative digital twin platform that integrates In-ternet of Things (IoT) sensors, artificial intelligence (AI), and machine learning (ML) al-gorithms to transform water distribution management practices. Our platform employsfour complementary predictive models including Long Short-Term Memory (LSTM) net-works, Facebook Prophet, LightGBM, and XGBoost to forecast water demand with achiev-ing 94.2% accuracy levels. The system incorporates real-time data collection from 450 IoTsensors across a metropolitan network that serves 750,000 residents and commercial enti-ties. A novel multi-objective optimization algorithm reduces operational costs by 28% whiledecreasing water loss by 15% through intelligent maintenance scheduling. Comprehensivecybersecurity protocols ensure data integrity and system resilience against various threats.Experimental validation conducted over 18 months demonstrates significant improvementsin predictive accuracy, operational efficiency, and environmental sustainability aspects. Theplatform achieves real-time response capabilities with sub-50ms latency and maintains 99.8%system availability throughout the deployment period.