Analysis of Optimal Sensor Placement in Looped Water Distribution Networks Using Different Water Quality Models

Piazza, Stefania; Sambito, Mariacrocetta; Freni, Gabriele

doi:10.3390/w15030559

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…There is a clear reliance of source localization on water quality and, hence, solute transport modeling. Piazza et al (2022) found that correctly accounting for dispersion significantly affected optimal sensor placement. Bartos and Kerkez (2021) demonstrate this link explicitly, using a finite difference solution to Eq.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Mixing in Sewer Networks for Source Localization

Sonnenwald

Shuttleworth

Bailey³

et al. 2023

J. Environ. Eng.

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There has been a recent increase of interest in sewer network water quality, both for pollutants and wastewater epidemiology. Of particular interest is the ability to perform cost-effective small-scale monitoring to understand the sewer network and perform source localization (the process of identifying the sources of materials of interest within the network), enabling prioritization of combined sewer overflow (CSO) interventions and targeted response to the detection of infectious diseases. Rhodamine WT fluorescent dye tracing was carried out in the combined sewer networks of four UK cities, for which network geometries were available. Over 100 dye concentration profiles were recorded, from which discharge, travel time (velocity), and dispersion were quantified. A simplified hydraulic and water quality (conservative solute transport) modeling approach was used to investigate dispersion further. A theoretical method for calculating dispersion over a reach with nonuniform properties was derived and used with the models and recorded data to develop a method for estimating the dispersion coefficient in sewers. Novel simultaneous injections into multiple manholes within one sewer network were conducted. Modeling of these injections validated the modeling approach and explained the measured concentration profiles, demonstrating both the potential of hydraulic and solute transport modeling and the new dispersion coefficient predictor for source localization. Such modeling can be used to develop sewer network "fingerprints" and source location probability plots based on residence time distribution (RTD) theory to maximize information from limited water quality monitoring. This will aid managers and operators in identifying potential intermittent sources of material within the network.

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Section: Introductionmentioning

confidence: 99%

Quantifying Mixing in Sewer Networks for Source Localization

Sonnenwald

Shuttleworth

Bailey³

et al. 2023

J. Environ. Eng.

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“…With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction [25][26][27][28][29]. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems [30][31][32][33][34]. Machine learning models have shown great potential in the estimation and prediction of water quality parameters, offering improved accuracy and efficiency compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

Ghoochani,

Khorram,

Nazemi

2023

Preprint

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Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.

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