Sensor Placement for Leak Localization in Water Distribution Networks using Machine Learning

2020 ◽  
Author(s):  
Rahul Madbhavi ◽  
Amit Joshi ◽  
Sai Munikoti ◽  
Laya Das ◽  
Pranab Kumar Mohapatra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Distribution ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Localization

scholarly journals Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

2018 ◽  
Vol 108 ◽  
pp. 152-162 ◽  
Author(s):  
Adrià Soldevila ◽  
Joaquim Blesa ◽  
Sebastian Tornil-Sin ◽  
Rosa M. Fernandez-Canti ◽  
Vicenç Puig
Keyword(s):  
Feature Selection ◽  
Water Distribution ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Localization

scholarly journals Compressed sensing based optimal sensor placement for leak localization in water distribution networks

2017 ◽  
Vol 20 (6) ◽  
pp. 1286-1295 ◽  
Author(s):  
Xiang Xie ◽  
Quan Zhou ◽  
Dibo Hou ◽  
Hongjian Zhang
Keyword(s):  
Compressed Sensing ◽  
Knapsack Problem ◽  
Water Distribution ◽  
Sensor Placement ◽  
Real Life ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Mutual Coherence ◽  
Optimal Sensor Placement ◽  
Leak Localization

Abstract The performance of model-based leak detection and localization techniques heavily depends on the configuration of a limited number of sensors. This paper presents a sensor placement optimization strategy that guarantees sufficient diagnosability while satisfying the budget constraint. Based on the theory of compressed sensing, the leak localization problem could be transformed into acquiring the sparse leak-induced demands from the available measurements, and the average mutual coherence is devised as a diagnosability criterion for evaluating whether the measurements contain enough information for identifying the potential leaks. The optimal sensor placement problem is then reformulated as a {0, 1} quadratic knapsack problem, seeking an optimal sensor placement scheme by minimizing the average mutual coherence to maximize the degree of diagnosability. To effectively handle the complicated real-life water distribution networks, a validated binary version of artificial bee colony algorithm enhanced by genetic operators, including crossover and swap, is introduced to solve the binary knapsack problem. The proposed strategy is illustrated and validated through a real-life water distribution network with synthetically generated field data.

scholarly journals Leak localization in water distribution networks using classifiers with cosenoidal features

2020 ◽  
Vol 53 (2) ◽  
pp. 16697-16702
Author(s):  
I. Santos-Ruiz ◽  
J. Blesa ◽  
V. Puig ◽  
F.R. López-Estrada
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Localization

scholarly journals First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 16691-16696
Author(s):  
Luis Romero ◽  
Joaquim Blesa ◽  
Vicenç Puig ◽  
Gabriela Cembrano ◽  
Carlos Trapiello
Keyword(s):  
Deep Learning ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
First Results ◽  
Leak Localization ◽  
Graph Based Clustering

scholarly journals Optimal Water Quality Sensor Placement by Accounting for Possible Contamination Events in Water Distribution Networks

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1999
Author(s):  
Malvin S. Marlim ◽  
Doosun Kang
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Sensor Placement ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Fair Value ◽  
Water Distribution Networks ◽  
Optimization Approach ◽  
Protection Measure ◽  
Contamination Events

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.

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