Cloud computing integration for leak diagnosis using meta-heuristic methods for water distribution networks

Abstract

© 2025 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 works.

We present the implementation of a CloudComputing-based system for leak diagnosis in a water distribution network. Pressure head/flow rate measured in field is stored and processed in a virtual machine to provide a leak diagnosis in two stages: 1) first the leak is detected by comparing the current operating conditions and expected nominal operating conditions obtained from a previously adjusted simulation model, and 2) the leak exact location and its magnitude are determined using a meta-heuristic method. The performance of the proposed system is implemented for an experimental hydraulic system at a laboratory scale. Results demonstrate a good accuracy in the leak diagnosis metrics at a reduced economic and computational cost, demonstrating the potential results from the implementation of a similar system in a large-scale water distribution network.

The authors gratefully acknowledge the financial support provided by the Tecnologico Nacional de M ´ exico and SECIHTI (Mexico). The authors also ácknowledge the support provided by SECTEI-CDMX through project eSAST, number 1564c23, DGAPA-UNAM through project IT1000724, and by the Spanish project SEAMLESS: Sustainable learning-based Management of Multi-resource Large-scale Systems (ref. PID2023-148840OB-I00), funded by MCIN/AEI/10.13039/501100011033/FEDER, UE.

Peer Reviewed

Postprint (author's final draft)