Smart Leak Detection in Water Distribution Networks Using Hybrid Deep Learning Models

Abstract

Water leakage in distribution networks poses significant challenges due to aging infrastructure, rising demand, and the limitations of conventional detection methods, resulting in substantial water loss and increased operational costs. This study proposes a hybrid deep learning approach that combines a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) for efficient leakage detection in water distribution networks. The CNN is utilized to automatically extract high-level features from multivariate sensor data, while the SVM performs robust classification to improve generalization and decision accuracy. A real-world water network dataset containing pressure, flow rate, and velocity measurements was used for model development. After data cleaning, feature selection, and Min–Max normalization, the dataset was split into training and testing sets. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Experimental results indicate that the proposed CNN–SVM hybrid model attains 95% accuracy and a ROC-AUC score of 97%, outperforming CNN models. The results confirm that integrating deep feature extraction with machine learning classification enhances leakage detection reliability. This approach provides a scalable and effective solution for real-time monitoring of water distribution networks and contributes to reducing non-revenue water and improving sustainable water resource management.