A robust clustering-based multi-objective model for optimal instruction of pipes replacement in urban WDN based on machine learning approaches

Water distribution networks (WDNs) face serious management challenges due to the high investment necessity for pipe maintenance and high performance as well as the uncertainties of input variables. To address these challenges, this study aims to prepare and implement the optimal instructions for pipe replacement with maximum hydraulic performance, minimum cost, and minimum uncertainty. Herein, a robust clustering multi-objective (RCMO) approach is developed by combining five models, including hydraulic simulation, multi-objective optimization, pipe failure rate prediction, non-linear interval programming, and multi-criteria decision-making. In this procedure, a clustering method is implemented to reduce the uncertain scenarios of the multi-objective optimization. The new approach is applied to a WDN in Gorgan, Iran. Implementing the optimal instruction increases the network's physical and hydraulic performance by 56% and 35%, respectively, and decreases the annual deficit of nodes' demand between 69% and 93%. Also, the proposed methodology reduces the optimization run time by about 99%.

Automated summary

This study aims to address the challenges of water distribution networks (WDNs), including high investment requirement for pipe maintenance, high performance, and uncertainties of input variables. A robust clustering multi-objective (RCMO) approach is developed, which combines five models to prepare and implement optimal instructions for pipe replacement with maximum hydraulic performance, minimum cost, and minimum uncertainty. The approach is applied to WDN in Gorgan, Iran, and the results show significant improvements in physical and hydraulic performance of the network, as well as a reduction in the annual deficit of nodes' demand. Additionally, the proposed methodology significantly reduces the optimization run time.