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

Abstract

ABSTRACT 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. 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