Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters

Abstract

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BOD_if), chemical oxygen demand (COD_if), total suspended solids (TSS_if), volatile suspended solids (VSS_if), and sediment (SED_ef) are used to predict EQPs, including COD_ef, BOD_ef, and TSS_ef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting COD_ef, BOD_ef, and TSS_ef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.

Keywords: Artificial neural networks; Deep learning; Optimization algorithms; Wastewater treatment plant.