Explainable deep learning models for predicting water pipe failures

Abstract

Failures within water distribution networks (WDNs) lead to significant environmental and economic impacts. While existing research has established various predictive models for pipe failures, there remains a lack of studies focusing on the probability of leaks and bursts. Addressing this gap, the present study introduces a new approach that harnesses deep learning algorithms — Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and TabNet for failure prediction. The study enhances these base models by optimising their hyperparameters using Bayesian Optimisation (BO) and further refining the models through data scaling. The Copeland algorithm and SHapley Additive exPlanations (SHAP) are also applied for model ranking and interpretation, respectively. Applying this methodology to Hong Kong's WDN data, the study evaluates the models' predictive performance across several metrics, including accuracy, precision, recall, F1 score, Matthews Correlation Coefficient (MCC), and Cohen's Kappa. Results demonstrate that BO significantly enhances the models' predictive abilities, such that the TabNet model's F1 score for leak prediction increases by 36.2% on standardised data. The Copeland algorithm identifies CNN as the most effective model for predicting both leak and burst probabilities. As indicated by SHAP values, critical features influencing model predictions include pipe diameter, material, and age. The optimised CNN model has been deployed as user-friendly web applications for predicting the probability of leaks and bursts, enabling both single-pipe and batch predictions. This research provides crucial insights for WDN management, equipping water utilities with sophisticated tools to forecast the probability of pipe failure, enabling more effective mitigation of such failures.