Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl<sub<2</sub<), and calcium chloride (CaCl<sub<2</sub<)], dissolve in runoff, travel downstream in the aqu...
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Autor*in:	Khurshid Jahan [verfasserIn] Soni M. Pradhanang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	stormwater artificial neural network (ANN) water quality road salts deicers

Übergeordnetes Werk:	In: Hydrology - MDPI AG, 2015, 7(2020), 4, p 80
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:4, p 80

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DOI / URN:	10.3390/hydrology7040080

Katalog-ID:	DOAJ020252277

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520			\|a Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl<sub<2</sub<), and calcium chloride (CaCl<sub<2</sub<)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.
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authorswithroles_txt_mv	Khurshid Jahan @@aut@@ Soni M. Pradhanang @@aut@@
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author	Khurshid Jahan
spellingShingle	Khurshid Jahan misc stormwater misc artificial neural network (ANN) misc water quality misc road salts misc deicers misc Science misc Q Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)
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topic_title	Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN) stormwater artificial neural network (ANN) water quality road salts deicers
topic	misc stormwater misc artificial neural network (ANN) misc water quality misc road salts misc deicers misc Science misc Q
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title	Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)
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title_full	Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)
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title_sort	predicting runoff chloride concentrations in suburban watersheds using an artificial neural network (ann)
title_auth	Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)
abstract	Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl<sub<2</sub<), and calcium chloride (CaCl<sub<2</sub<)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.
abstractGer	Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl<sub<2</sub<), and calcium chloride (CaCl<sub<2</sub<)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.
abstract_unstemmed	Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl<sub<2</sub<), and calcium chloride (CaCl<sub<2</sub<)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.
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title_short	Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)
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up_date	2024-07-03T13:52:29.590Z
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Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

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