Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems

Xing, Lu; Sela, Lina

doi:10.1016/j.watres.2019.03.051

Cited by 32 publications

(5 citation statements)

References 40 publications

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“…Previous cluster-based studies have mainly focused on detecting pressure, demand, pipe burst, infrastructure damage, and illicit intrusion in water distribution systems (Perelman and Ostfeld 2012;Sambito et al 2019;Wu and Liu 2020;Xing and Sela 2019). In the clustering analysis here, the features, such as the length of time-series water depth from UDSs, are found to be negatively correlated with the number of clusters.…”

Section: Discussionmentioning

confidence: 78%

“…Wu et al (2016) adopted the clustering algorithm, developed by Rodriguez & Laio (2014), to detect the shortduration pipe burst with a 0.61% false positive in water distribution systems. Xing & Sela (2019) selected SC (Silhouette Coefficient) and CHI (Calinski-Harabasz Index) as the metrics to evaluate K-mean Clustering (KC) performance in clustering time-series water pressure data and they finally identified the number of clusters for the pressure sensor placement. However, it was unclear why 6 they chose these two indexes as the UMLA performance metrics.…”

Section: Introductionmentioning

confidence: 99%

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A Performance Comparison of Unsupervised Machine Learning Algorithms for Clustering Water Depth Datasets at Urban Drainage Systems

Li¹,

Brewer²

2020

Preprint

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As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have been drawn tremendous interest in infrastructure monitoring, flow prediction, and pollutant warning recently. However, most of them are applied in water distribution systems, and few studies consider using unsupervised clustering analysis to group the time-series hydraulic-hydrologic data at urban drainage systems. To improve the understanding of how clustering analysis contributes to detecting urban flooding events, this study compared the performance of K-means Clustering, Agglomerative Clustering, and Spectral Clustering in uncovering time-series water depth similarity and finally identified the number of clusters with maximum performance scores. In this work, the water depth datasets are simulated by a real-world SWMM model and then formatted for a clustering problem. Three standard performance evaluation scores, the SCI, CHI, and DBI, are employed to assess the clustering performance under six artificial rainfalls and two recorded storms. The results indicate that SCI and DBI are appropriate for assessing the performance of K-means Clustering and Agglomerative Clustering, while CHI only works for Spectral Clustering. Noticeably, it was found that the number of clusters is negatively related to the dataset length, but less correlated with the dataset magnitude.

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Section: Discussionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

A Performance Comparison of Unsupervised Machine Learning Algorithms for Clustering Water Depth Datasets at Urban Drainage Systems

Li¹,

Brewer²

2020

Preprint

View full text Add to dashboard Cite

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“…Commercial departments now have a variety of hardware-based leak detection equipment (Aghda et al 2018). Similarly, software-based leak detection algorithms have been proposed in recent studies, including steady-state and transient-state (Zhou et al 2019, Xing et al 2019. Hardware-based leak detection technology equipment can be roughly divided into " out of tube " or external equipment and " in tube " or robot equipment.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network

Wang

2022

Water Resour Manage

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In the water supply network, leakage of pipes will cause water loss and increase the risk of environmental pollution. For water supply systems, identifying the leak point can improve the efficiency of pipeline leak repair. Most existing leak location methods can only locate the leak point approximately to the node or pipe section of the pipe network, but cannot locate the specific location of the pipe section. This paper presents a framework for accurate location of water supply network leakage based on ResNet. The framework is to pinpoint leaks to specific locations along the pipeline. The leakage of two kinds of pipe networks is simulated.For a pipe network containing 40 pipes, the positioning accuracy of the pipe section is 0.94, and the MSE of the specific location of the leakage point is 0.000435. For the pipe network containing 117 pipes, the positioning accuracy of the pipe section is 0.91, and the MSE of the specific location of the leakage point is 0.0009177, and the leak location ability under different sensor arrangements is analyzed. Experiments verify the robustness and applicability of the framework.

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“…At present, data mining methods [ 26 – 28 ] have gradually become a major breakthrough in knowledge discovery. The data mining methods can find the hidden information that the data cannot tell us, and obtain the previously unknown and valuable knowledge.…”

Section: Introductionmentioning

confidence: 99%

The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

et al. 2021

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Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.

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Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems

Cited by 32 publications

References 40 publications

A Performance Comparison of Unsupervised Machine Learning Algorithms for Clustering Water Depth Datasets at Urban Drainage Systems

A Performance Comparison of Unsupervised Machine Learning Algorithms for Clustering Water Depth Datasets at Urban Drainage Systems

An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network

The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

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