Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Yu, Jie; Xu, Lei; Xie, Xinyou; Hou, Dibo; Huang, Pingjie; Zhang, Guangxin; Zhang, Hongjian

doi:10.3390/w9110894

Cited by 3 publications

(3 citation statements)

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“…The proposed model outperformed benchmark models, and the results showed that the detection capacity significantly improved. Yu et al [2] proposed a method for detecting water pollution using multi-station spatial and temporal data. WDS consists of multiple stations with large-scale characteristics and high complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Water distribution systems (WDS) are vital to cities' water supply, particularly given that their safety directly affects public health. However, the water quality is hard to maintain in WDS due to the effects of the distance and time taken during the long process of delivery from the water source to the end-users [2]. Besides, typically major parts of WDS exist in the open environment; thus, it is vulnerable to external disturbances, such as sabotage.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Water Quality Classification in Water Distribution Networks

Shahra

Basurra

et al. 2021

Proceedings of the International Neural Networks Society

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Maintaining high water quality is the main goal for water management planning and iterative evaluation of operating policies. For effective water monitoring, it is crucial to test a vast number of drinking water samples that is time-consuming and labour-intensive. The primary objective of this study is to determine, with high accuracy, the quality of drinking water samples by machine learning classification models while keeping computation time low. This paper aims to investigate and evaluate the performance of two supervised classification algorithms, including artificial neural network (ANN) and support vector machine (SVM) for multiclass water classification. The evaluation uses the confusion matrix that includes all metrics ratios, such as true positive, true negative, false positive, and false negative. Moreover, the overall accuracy and f1-score of the models are evaluated. The results demonstrate that ANN outperformed the SVM with an overall accuracy of 94% in comparison to SVM, which shows an overall accuracy of 89%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Water Quality Classification in Water Distribution Networks

Shahra

Basurra

et al. 2021

Proceedings of the International Neural Networks Society

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“…Multi‐information fusion (MIF) is a consensus‐based decision support technique, which provides cross‐validated decisions that are reliable to deal with non‐uniqueness in PSD (Dickerson et al., 2007; Kazai et al., 2013; Yu et al., 2017). This study aims to utilize MIF to make full use of the difference between the diffusion coefficient and the attenuation coefficient of COD Mn , NH 3 ‐N, and Total‐P indicators for cross‐validation (Perelman et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

A Decision Support Framework for Pollution Source Detection via Coupled Forward‐Inverse Optimization and Multi‐Information Fusion

Zhu

Sun

et al. 2023

Water Resources Research

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Due to the uncertainty in sensor data, low model accuracy, and high parameter heterogeneity in water quality modeling, pollution source detection (PSD) typically results in a problem of multiple possible solutions, which is the so‐called non‐uniqueness effect. Identifying unique solution to PSD problems is fundamentally essential for water quality control in surface water and groundwater systems. This study proposes a decision support framework to reduce the impact of uncertainty and identify a unique solution using a consensus‐based multiple information fusion method. Multiple water quality information sources are fused in the framework via spatial clustering and temporal Bayesian updating. Considering the real‐world complexity, the recession‐curve displacement method is used to handle multi‐sources scenario to enhance the accuracy of the framework. Meanwhile, a coupled forward‐inverse model instead of random sampling is used to improve the solving efficiency of PSD. The framework is validated by a real water pollution event and a number of semi‐hypothetical case studies. The results show that the prediction accuracy of the single‐source and double‐source pollution problems are 88.40% and 82.69%, with an average relative error of 9.75% and 11.42%, respectively. In addition, the uncertainty contribution quantified by a variance decomposition approach suggests that the interaction between parameter and measurement uncertainties has the greatest impact on the PSD results. The results reveal the advantages of the proposed decision framework for government organizations and communities involved in water environment management.

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