Determination of key sensor locations for non-point pollutant sources management in sewer network

Kang, OnYu; Lee, Seung−Chul; Wasewar, Kailas L.; Kim, Minjeong; Liu, Hongbin; Oh, Tong In; Janghorban, Emad

doi:10.1007/s11814-012-0108-y

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Monitoring site selection (or sensor placement) has been widely studied for drinking water networks, but only a few studies exist on sewage networks. Kang et al (2013) determined key sensor locations for non-point pollutant sources management in sewage networks by means of clustering analysis and ANOVA on top of SWMM simulated results. A few examples exist on sensor placement for illicit intrusion detection in sewage networks based on single and multi-objective optimization (Yazdi, 2018) or on Bayesian decision networks (Sambito et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

Calle

Martínez

Brugués-i-Pujolràs

et al. 2021

Environment International

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Section: Introductionmentioning

confidence: 99%

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

Calle

Martínez

Brugués-i-Pujolràs

et al. 2021

Environment International

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“…In the last decade, many scholars have introduced a number of machine learning techniques to investigate the available water resources and hydrological datasets (Diao et al 2014;Hsu et al 2013;Kang et al 2013;Mullapudi & Kerkez 2018;Wang et al 2009). Bowes et al (2019) compared long short-term memory and recurrent neural network by using a time-series of groundwater table data in the city of Norfolk, Virginia.…”

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

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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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“…For rivers and lakes in urban areas, NPS pollution in the form of runoff from urban areas has contributed greatly to the degradation of flow in the receiving water bodies (Sartor et al 1972;Kang et al, 2013). NPS pollution in urban areas has become an important issue for aquatic environments and has therefore received increased attention in recent years (Rossi et al, 2006;Kang et al, 2013). NPS pollution in urban areas is not easily identified and characterized which adds significantly to the issues of source analysis, load calculation, and distribution simulation.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of non-point source nitrogen in urban area of Beijing City, China

Ding

Gong

et al. 2013

Environ Syst Res

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Background: Non-point source (NPS) pollution, has been dominant in many urban areas, causing nutrient loss, water body pollution and aqueous systems damaging. Among the pollutants, nitrogen is a key nutrient which can cause eutrophication of rivers, lakes and reservoirs. Beijing, the capital of China, has a booming economy and a huge population. Various human activities have affected nitrogen accumulation deeply and caused considerable NPS pollution. This research calculated the annual load of NPS nitrogen in urban area of Beijing City (UABC) in 2005 and simulated its spatial distribution, which may be useful for environmental planning and pollution control of the UABC and cities with intensive human activities. Results: The total NPS nitrogen load of the UABC was is 1083.09 t in 2005. The load of agricultural land, construction land and unused land were 92.04 t, 969.29 t and 21.77 t. As far as spatial distribution is concerned, construction land was with heavy pollution. Chaoyang district, Haidian district and Fengtai district were the most major export regions of NPS nitrogen loads. The loads of them were 376.88 t, 286.87 t and 249.92 t. As for load intensity of NPS nitrogen, the high-load sources were distributed in the Xicheng district and Dongcheng district, of which the load intensities were 1.10 km 2 /t and 1.11 km

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Determination of key sensor locations for non-point pollutant sources management in sewer network

Cited by 7 publications

References 15 publications

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

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

Spatial distribution of non-point source nitrogen in urban area of Beijing City, China

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