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

• 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/

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.