A Novel Approach for Leak Localization in Water Distribution Networks Using Computational Intelligence

Sánchez-Rivero, Maibeth; Quiñones-Grueiro, Marcos; Rosete-Suárez, Alejandro; Llanes‐Santiago, Orestes

doi:10.1007/978-3-030-34409-2_6

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, conducted by Zhang et al,73 obtained a high detection rate but without any localization accuracy. Besides, Sanchez et al74 concentrated on the performance of three metaheuristic algorithms based on the network topology configuration in order to permit an accurate leak localization. But, this technique is, however, sensitive to network characteristics and certain errors that may affect either the hydraulic model or the network topology leading to inaccurate leak localization outcomes.…”

mentioning

confidence: 99%

A genetic algorithm‐based intelligent solution for water pipeline monitoring system in a transient state

Abdelhafidh

Fourati

2020

Concurrency and Computation

View full text Add to dashboard Cite

Water pipeline monitoring system becomes a relevant solution to cope with various pipeline hydraulic failures in order to save the environment from water losses. In this respect, cognitive water distribution system (WDS) combines Internet of Things (IoT) technology with Big Data generated by various connected objects and devices for reliable structural health monitoring of pipelines. Accordingly, designing a scalable WDS with smart leak detection and localization requires a serious study and an adequate planning. In this paper, we suggest a cognitive IoT-based architecture with adequate data collection based on the Apache Spark framework. Furthermore, our objective is to elaborate a hybrid mechanism that defines and performs accurate leakage identification and localization by taking into account both the steady-state and transient behavior of water. The bio-inspired sensitivity analysis of the water pressure profile based on the genetic algorithm ensures the effectiveness and the accuracy of the proposed monitoring solution.

show abstract

mentioning

confidence: 99%