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Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques

Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop...

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Main Authors: Abdalrahman, Ghada, Lai, Sai Hin, Kumar, Pavitra, Ahmed, Ali Najah, Sherif, Mohsen, Sefelnasr, Ahmed, Chau, Kwok Wing, Elshafie, Ahmed
Format: Article
Published: Taylor & Francis 2022
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
TJ Mechanical engineering and machinery
Online Access:http://eprints.um.edu.my/32715/
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spelling my.um.eprints.327152022-08-18T02:10:42Z http://eprints.um.edu.my/32715/ Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques Abdalrahman, Ghada Lai, Sai Hin Kumar, Pavitra Ahmed, Ali Najah Sherif, Mohsen Sefelnasr, Ahmed Chau, Kwok Wing Elshafie, Ahmed QA75 Electronic computers. Computer science T Technology (General) TJ Mechanical engineering and machinery Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error. Taylor & Francis 2022-12-31 Article PeerReviewed Abdalrahman, Ghada and Lai, Sai Hin and Kumar, Pavitra and Ahmed, Ali Najah and Sherif, Mohsen and Sefelnasr, Ahmed and Chau, Kwok Wing and Elshafie, Ahmed (2022) Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques. Engineering Applications of Computational Fluid Mechanics, 16 (1). pp. 397-421. ISSN 1994-2060, DOI https://doi.org/10.1080/19942060.2021.2019126 <https://doi.org/10.1080/19942060.2021.2019126>. 10.1080/19942060.2021.2019126
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
T Technology (General)
TJ Mechanical engineering and machinery
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
TJ Mechanical engineering and machinery
Abdalrahman, Ghada
Lai, Sai Hin
Kumar, Pavitra
Ahmed, Ali Najah
Sherif, Mohsen
Sefelnasr, Ahmed
Chau, Kwok Wing
Elshafie, Ahmed
Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
description Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error.
format Article
author Abdalrahman, Ghada
Lai, Sai Hin
Kumar, Pavitra
Ahmed, Ali Najah
Sherif, Mohsen
Sefelnasr, Ahmed
Chau, Kwok Wing
Elshafie, Ahmed
author_facet Abdalrahman, Ghada
Lai, Sai Hin
Kumar, Pavitra
Ahmed, Ali Najah
Sherif, Mohsen
Sefelnasr, Ahmed
Chau, Kwok Wing
Elshafie, Ahmed
author_sort Abdalrahman, Ghada
title Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
title_short Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
title_full Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
title_fullStr Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
title_full_unstemmed Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
title_sort modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques
publisher Taylor & Francis
publishDate 2022
url http://eprints.um.edu.my/32715/
_version_ 1744649145427165184
score 13.211869

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