Network Embedding: The Bridge Between Water Distribution Network Hydraulics and Machine Learning

Abstract

Machine learning has been increasingly used to solve management problems of water distribution networks (WDNs). A critical research gap, however, remains in the effective incorporation of WDN hydraulic characteristics in machine learning. Here we present a new water distribution network embedding (WDNE) method that transforms the hydraulic relationships of WDN topology into a vector form to be best suited for machine learning algorithms. The nodal relationships are characterized by local structure, global structure and attribute information. A conjoint use of two deep auto-encoder embedding models ensures that the hydraulic relationships and attribute information are simultaneously preserved and are effectively utilized by machine learning models. WDNE provides a new way to bridge WDN hydraulics with machine learning. It is first applied to a pipe burst localization problem. The results show that it can increase the performance of machine learning algorithms, and enable a lightweight machine learning algorithm to achieve better accuracy with less training data compared with a deep learning method reported in the literature. Then, applications in node grouping problems show that WDNE enables machine learning algorithms to make use of WDN hydraulic information, and integrates WDN structural relationships to achieve better grouping results. The results highlight the potential of WDNE to enhance WDN management by improving the efficiency of machine learning models and broadening the range of solvable problems. Codes are available at https://github.com/ZhouGroupHFUT/WDNE