Detection of Silent Water Leaks in Household Using Artificial Intelligence Methods

Abstract

Water losses in distribution systems constitute a significant global challenge, undermining water resource sustainability, increasing operational costs, and threatening the water security of millions. In Latin America, up to 40% of treated water is reportedly lost due to leaks, ruptures, and defective connections (Xylem, 2025). At the household level, silent leaks—particularly in toilet flushing systems—can waste over 37,850 litres annually per dwelling (US EPA, 2024). Various international studies have addressed early leak detection using intelligent systems. In Europe, wireless sensor networks and machine learning models such as Random Forest, Support Vector Machines, and neural networks have been deployed for anomaly detection in urban networks. Asian research has demonstrated detection accuracies exceeding 97% through convolutional neural networks trained on acoustic and vibrational signals, enhanced by contrastive learning to address data scarcity. Hybrid approaches combining hydraulic modelling with AI have been applied in the Middle East and China, whereas logic-based and anomaly detection algorithms have been integrated into real-time platforms in Australia and Canada. Sensor placement optimisation via graph partitioning has further improved coverage efficiency. Despite their effectiveness, these solutions often require substantial investment and advanced infrastructure, limiting their applicability in resource-constrained environments. This study proposes a cost-effective, perceptron-based model for detecting silent leaks in household toilets, integrated within an Internet of Things (IoT) framework. The system employs a Hall-effect flow sensor to capture high-resolution filling-time and pulse-count data, processed through supervised learning to discriminate between normal consumption and leakage. Experimental results under real-use conditions achieved 98% classification accuracy, demonstrating both technical feasibility and operational suitability. This approach offers a practical, computationally efficient solution for domestic contexts in Latin America, enabling real-time monitoring and immediate user alerts, thus supporting water conservation efforts through accessible intelligent detection.