Pressure and Water Quality Integrated Sensor Placement Considering Leakage and Contamination Intrusion within Water Distribution Systems

Mu, Tianwei; Huang, Manhong; Tan, Haoqiang; Chen, Gang; Zhang, Rui

doi:10.1021/acsestwater.1c00209

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…After the PCA dimensionality reduction, cluster analysis can be processed with respect to the new weight matrix. A fuzzy C-means (FCM) is an unsupervised learning model presented in 1973 [26,27], which does not require manual creation of categories for dataset labels. e FCM algorithm is an effective cluster model based on a fuzzy clustering algorithm to minimize an objective function, dividing data into different classes by the degree of membership.…”

Section: Fuzzy C-means (Fcm) Clusteringmentioning

confidence: 99%

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Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Jing

Wang

2022

Security and Communication Networks

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Network available and accessible is of great importance to the Internet of things (IoT) devices. In this study, a novel machine learning method is presented to predict the occurrence of distributed denial-of-service (DDoS) attacks. Firstly, a structure of edges and vertices within graph theory is created to simultaneously extract traffic data characteristics. Eight characteristics of traffic data are selected as input variables. Secondly, the principal component analysis (PCA) model is adopted to extract DDoS and normal communication features further. Then, DDoSs are detected by fuzzy C-means (FCM) clustering with these features. In the case study, 2000 traffic data in dataset CICIDS-2017 are used to verify the practicability of this method. The results of recall, false positive, true positive, true negative, and false negative are 100.00%, 1.05%, 68.95%, 0.00%, and 30.00%. Compared with other methods, the results demonstrate that the detecting reliability is improved, and the method has a good effect on the detection of DDoS attacks.

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Section: Fuzzy C-means (Fcm) Clusteringmentioning

confidence: 99%

“…With respect to the center of clustering, the membership matrix can be revised via solving the Euler distance [27]:…”

Section: Fuzzy C-means (Fcm) Clusteringmentioning

confidence: 99%

Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Jing

Wang

2022

Security and Communication Networks

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“…After the large-scale network partition, an EPLD model is presented to optimize the locations of pressure sensors via the EPS process published before in our group. , The normal pressures at each time are first solved via hydraulic calculation. Then, a node is randomly selected as a sensor in each partition.…”

Section: Methodsmentioning

confidence: 99%

Partitioning Leakage Detection in Water Distribution Systems: A Specialized Deep Learning Framework Enhanced by Spatial–Temporal Graph Convolutional Networks

Mu,

Zhang,

Huang

et al. 2024

ACS EST Water

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Effective leakage detection is crucial for ensuring operational efficiency, reducing water loss, and maintaining infrastructure integrity in water distribution systems (WDSs). This study presents a specialized leakage detection approach enhanced by spatial−temporal graph convolutional networks (ST-GCN). This method combines large-scale network partition, optimized sensor placement, pilot-scale network partition, and the ST-GCN model, which captures both spatial and temporal dependencies. Then, two case studies are employed to evaluate the effectiveness of this method. The model achieved an average accuracy, precision, recall, and F1-score of 98.38, 98.89, 97.95, and 98.41% across multiple tests for Network A and of 98.51, 98.51, 98.56, and 98.53% for Network B, respectively, which demonstrate the model's high performance. Furthermore, it compares the model's simulation results with three existing methods. The enhanced ST-GCN model is superior to those of the other models in terms of accuracy, confirming its superior effectiveness in detecting leakages.

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“…where N is the number of nodes in a WDS. Due to the great variation in water base demands across various nodes, the pressure sensitivity can differ by orders of magnitude [28]. Thus, standardization should be implemented.…”

Section: Pressure Sensitivity Matrixmentioning

confidence: 99%

A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

Dong,

Shu,

2024

Water

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This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings.

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Pressure and Water Quality Integrated Sensor Placement Considering Leakage and Contamination Intrusion within Water Distribution Systems

Cited by 7 publications

References 33 publications

Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Partitioning Leakage Detection in Water Distribution Systems: A Specialized Deep Learning Framework Enhanced by Spatial–Temporal Graph Convolutional Networks

A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

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