Pressure sensor placement for leak localization using simulated annealing with hyperparameter optimization

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents a machine learning method for optimal pressure sensor placement in water distribution networks. The proposed approach considers annealing meta-heuristics, and it is focused on the optimal placement of the sensors to perform leak localization. Unlike other works, this method considers a limited number of sensors to be placed and some restrictions on critical nodes that can be excluded or preselected. The approach is based on minimizing a cost function; this cost function is assigned as the leak location error, which varies depending on the subset of nodes where the sensors are assigned and the configuration of the leak location method. A leak localization technique based on k-NN classifiers was used, and during the minimization of the cost function, classifier hyperparameters were simultaneously optimized. The proposed method was tested on the Hanoi water distribution network programmed in MATLAB.Postprint (author's final draft