Predication of Water Pollution Peak Concentrations by Hybrid BP Artificial Neural Network Coupled with Genetic Algorithm

Abstract

ABSTRACT Water pollutions can severely affect water environment, causing water quality degradation and threatening aquatic wildlife. Deemed as guideline for maximum environmental impact assessment, water pollution peak concentration (WPPC) has been intensively studied to organize effective countermeasures. In this study, a back propagation artificial neural network (BPANN) coupled with genetic algorithm (GA) was constructed to predict peak concentrations. Compared with BPANN, multiple linear regressions model (MLRM) and step-wise multiple linear regressions model (SMLRM), GA-BPANN model showed superior accuracy in both simulating and predicting peak concentrations (R 2  = 0.93 and 0.67 0.69 respectively). In 12 peak concentration cases, GA-BPANN model’s mean absolute relative error (MARE) ranges from 0.0 to 0.58, averaged at 0.09, significantly lower than BPANN, MLRM and SMLRM (MARE = 0.29, 0.45 and 0.48). Further analysis revealed that GA-BPANN model can be used as an effective and efficient tool for water quality simulation and early warning prediction.