AI-driven IoT and gamification for smart water management: Real-time monitoring and predictive analytics

Abstract

Water conservation remains a pressing global challenge, worsened by inefficient usage and delayed leak detection. This study presents the Smart Gamified Water Conservation System (SGWCS), a novel framework that integrates IoT-based water metering, AI-driven analytics, and adaptive user engagement. SGWCS employs a CNN-Attention-LSTM model for real-time demand forecasting, achieving 97.2% accuracy, and a hybrid rule-ML anomaly detection system with 92.8% sensitivity, reducing false positives by 38% in industrial trials. A gamification module with AI-personalized nudges increased user participation by 28% and led to an average 12.5% reduction in residential water use. The system was deployed across residential, industrial, and municipal sites in Raipur, India, using a privacy-by-design edge-cloud architecture. Evaluation metrics include prediction accuracy, leak detection performance, and user retention over time. These results demonstrate SGWCS as a scalable, intelligent, and ethically deployable platform for data-driven water resource optimization.