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

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dc.citedby10
dc.contributor.authorAbdalrahman G.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorChau K.W.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57220870889en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:41:49Z
dc.date.available2023-05-29T09:41:49Z
dc.date.issued2022
dc.description.abstractPredicting 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. Abbreviations: IR: Infiltration Rate; TWW: Treated Wastewater; TSS: Total Suspended Solids; BOD: Biological Oxygen Demand; EC: Electric Conductivity; HC: Hydraulic Conductivity; TN: Total Nitrogen; TP: Total Phosphorous; HLR: Hydraulic Loading Rate; ANN: Artificial Neural Network; MLP: Multilayer Perceptron Model; ENN: Elman Neural Network; FFANN: Feedforward Artificial Neural Networks; R: Regression Values; SAR: Sodium Adsorption Ratio; DOC: Dissolved Organic Carbon; ANAMMOX: Anaerobic Ammonium Oxidation; CEC: Cation Exchange Capacity; BPNN: Back Propagation Neural Network; GRNN: General Regression Neural Networks; ELM: Extreme Learning Machine Neural Networks; TDNN: Time Delay Neural Network; TLRN: Time Lag Recurrent Network; NGWTP: North Gaza Wastewater Treatment Plant; MASL: Meters Above Sea Level; DNC: Dynamic Node Creation; PWA: Palestinian Water Authority; RBF: Radial Basis Function; ANFIS: Adaptive Neuro Fuzzy Inference System; BD: Bulk Density; RMSE: Root Mean Square Error; MAE: Mean Absolute Error; MSE: Mean Square Error; R 2: Determination Coefficient; LLR: Local Linear Regression; DLLR: Dynamic Linear Regression; MNN: Modular Neural Networks; RNN: Recurrent Neural Network; NARX: Nonlinear Autoregressive with Exogenous input network; WNN: Wavelet Neural Networks. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2021.2019126
dc.identifier.epage421
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85124168155
dc.identifier.spage397
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124168155&doi=10.1080%2f19942060.2021.2019126&partnerID=40&md5=c2e85c01fa98038aba5d674bcca8f930
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27265
dc.identifier.volume16
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleModeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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