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

• Published in: Environmental science and pollution research international Vol. 30; no. 44; pp. 99362 - 99379
• Main Authors: Sheikh Khozani, Zohreh, Ehteram, Mohammad, Mohtar, Wan Hanna Melini Wan, Achite, Mohammed, Chau, Kwok-wing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Biochemical oxygen demand; Chemical oxygen demand; Codification; Data points; Deep learning; Earth and Environmental Science; Ecotoxicology; Effluents; Environment; Environmental Chemistry; Environmental Health; Environmental monitoring; Hydrologic cycle; Kernels; Machine learning; Mathematical models; Neural networks; Oxygen; Parameters; Radial basis function; Research Article; rivers; sediments; Solid suspensions; swarms; Total suspended solids; uncertainty; Waste Water Technology; Wastewater treatment; Wastewater treatment plants; Water Management; Water Pollution Control; Deep learning; Optimization algorithms; Wastewater treatment plant; Artificial neural networks
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-29406-8

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.