Pressure sensor placement in water distribution networks for leak detection using a hybrid information-entropy approach

Khorshidi, Majid Akbarzadeh; Nikoo, Mohammad Reza; Taravatrooy, Narges; Sadegh, Mojtaba; Al-Wardy, Malik; Al-Rawas, Ghazi

doi:10.1016/j.ins.2019.12.043

Cited by 38 publications

(8 citation statements)

References 37 publications

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“…This paper applies the information entropy [16][17][18][19][20] to analyze the evaluation index weight of the engineering practical ability of college students majoring in CAD.…”

Section: Evaluation Index Weight Processingmentioning

confidence: 99%

Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

Liu¹

2020

Int. J. Emerg. Technol. Learn.

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Currently, the college students majoring in computer-aided design (CAD), a discipline under the department of mechanical engineering, generally have a poor ability to solve engineering problems. Therefore, this paper attempts to design effective training strategies to improve the practical ability of the CAD majors. Firstly, the authors highlighted the promoting effect of the CAD on the practical ability of college students, and summed up the prob-lems of college students in the engineering application of the CAD. On this basis, several strategies were put forward to improve the practical ability of the CAD majors from three aspects: CAD curriculum planning, CAD practi-cal training and CAD platform construction. To verify the effectiveness of the strategies, an index system was established based on information entro-py, and organized into an evaluation model through expert scoring. The strategies and evaluation model provide strong supports to the training of practical ability for the CAD majors.

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“…This paper applies the information entropy [16][17][18][19][20] to analyze the evaluation index weight of the engineering practical ability of college students majoring in CAD.…”

Section: Evaluation Index Weight Processingmentioning

confidence: 99%

Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

Liu¹

2020

Int. J. Emerg. Technol. Learn.

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“…Additionally, a number of studies considered strategies for optimal sensor placement since it greatly influences leak detection and localization methods efficiency. The optimization approach is most widely used, and thus different enhancements were considered, such as a clustering process prior to optimization [12], hybrid feature selection method [13], methods that reduce the optimization search space [14], and an investigation of the influence of measurement uncertainty [15]. A detailed overview of leakage detection methodologies can be found in review papers by Wu and Liu [16], Chan et al [17], and Zaman et al [18].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

Lučin

Čarija

et al. 2021

Mathematics

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In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of ±2.5%). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation ±20% the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of ±10% the accuracy increased from 36% to 77%).

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“…However, the algorithms are minimal for the quantity and quality of the available data. Ideally, it is possible to place sensors at each node of the WDN, but the installation, monitoring, and maintenance of such sensors is not possible [4]. Even the most instrumented networks have a minimal number of sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Some of them consider metaheuristic such as genetic algorithms [9], [10] or particle swarm optimization [11]. Other works are focused on minimizing the error in the leak detection based on the information theory [4], evolutionary algorithms [12], decision-making techniques, such as DEMATEL combined with fuzzy logic [13], among others. Recently, some works propose multi-target sensor placement algorithms to minimize both the error in leak detection and the error in leak localization [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Pressure Sensor Placement for Leak Localization Using Simulated Annealing with Hyperparameter Optimization

Morales-Gonzalez

Santos‐Ruiz

López‐Estrada

et al. 2021

2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol)

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This paper presents a machine learning method for optimal pressure sensor placement in water distribution networks. The proposed approach considers annealing metaheuristics, 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.

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Pressure sensor placement in water distribution networks for leak detection using a hybrid information-entropy approach

Cited by 38 publications

References 37 publications

Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

Pressure Sensor Placement for Leak Localization Using Simulated Annealing with Hyperparameter Optimization

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