A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong

The water distribution network is one of the most pivotal services for communities, and that's why its effective and secure operation is crucial to the growth of national and global economies. To this end, the paramount objective of this study is to devise an automated self-adaptive model for deterioration prediction of saltwater pipes. The developed model is envisioned on coupling deep learning network with Bayesian optimization (HBO-DL) for forecasting the condition of different material categories of saltwater pipes stepping on their pip-related, soil-related, operational-related, and environmental-related features. In this regard, Bayesian optimization is leveraged to amplify the training mechanism of deep learning neural network through iterative optimization of its hyper parameters. The developed model is validated through several folds of validation that encompass performance evaluation, statistical analysis, graphical comparison, and unified ranking. The conducted comparative analysis evinced that the developed HBO-DL model managed to significantly perform better than feed forward neural network, support vector machines and Gaussian process regression by 76.85%, 73.31% and 79.08%, respectively. The developed prediction model can stand as a practical and useful tool to forecast the condition performance of water networks which aids municipalities in designing optimum intervention plans evading socioeconomic losses elicited from pipe failures.