Optimal Placement of Water Quality Monitoring Stations in Sewer Systems: An Information Theory Approach

Banik, Bijit Kumar; Alfonso, Leonardo; Torres, Adriano; Mynett, Arthur E.; Cristo, Cristiana Di; Leopardi, Angelo

doi:10.1016/j.proeng.2015.08.956

Cited by 24 publications

(13 citation statements)

References 27 publications

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“…A second line of research involves the design of planning methods for the deployment of a set of sensors in a given network [ 1 , 23 , 24 , 25 , 26 , 27 ] so that the arrangement of the deployed sensors maximizes the likelihood of detecting any anomaly. Furthermore, some research was done on the topic of portioning WDSs.…”

Section: Introductionmentioning

confidence: 99%

Pollution Source Localization in Wastewater Networks

Chachuła

Nowak

Solano

2021

Sensors

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In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.

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Section: Introductionmentioning

confidence: 99%

Pollution Source Localization in Wastewater Networks

Chachuła

Nowak

Solano

2021

Sensors

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“…Improper management of a river's pollution could result in significant damage to the flora and fauna of the ecosystem, the long term impacts on public health, and distraction to commerce and the economy, which in turns lead to the overall disruption of a nation's way of life [6]. The regular monitoring of water quality in a river network is crucial for reliable water supply and preservation of a healthy ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

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“…Global climate change has affected the intensity and patterns of rainfall, which in turn has affected runoff (May 2008;Hammond et al 2015). This has led to frequent flooding, causing heavy economic losses and casualties (Banik et al 2015). In July 2012, Beijing registered a flood emergency caused by road inundation.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Yan

Feng

et al. 2021

Int J Disaster Risk Sci

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Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

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Optimal Placement of Water Quality Monitoring Stations in Sewer Systems: An Information Theory Approach

Cited by 24 publications

References 27 publications

Pollution Source Localization in Wastewater Networks

Pollution Source Localization in Wastewater Networks

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

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