Application of Artificial Neural Networks to the Prediction of Total Suspended Solids (TSS) and Chemical Oxygen Demand (COD) at an Urban Wastewater Pumping Station in the Abidjan District (C？te d’Ivoire)

Conventional wastewater characterization relies on time-consuming and costly physicochemical analyses that are poorly suited to real-time monitoring. In this context, turbidity is an easily measurable optical indicator with potential for the indirect estimation of global pollution parameters. This study aimed to develop artificial neural network (ANN) models to predict total suspended solids (TSS) and chemical oxygen demand (COD) concentrations using turbidity as the sole input variable. Two hundred (200) wastewater samples were collected at the Blockauss pumping station in Abidjan during different seasons. Turbidity, TSS, and COD were determined according to NF EN ISO 7027, NF T 90-105, and CEAEQ methods. The normalized dataset was divided into training (50%), validation (25%), and testing (25%) subsets. Multilayer perceptrons with 1 to 15 hidden neurons were optimized and evaluated using R, R², RMSE, and MRD. The selected 1-1-1 architecture for TSS showed good performance (R² = 0.80; RMSE = 0.127; MRD = 7.46%). For COD, the 1-2-1 architecture yielded more moderate performance (R² = 0.66; RMSE = 0.246; MRD = 12.83%), indicating that turbidity explained a smaller proportion of COD variability, owing to the complex composition of wastewater and the influence of environmental and climatic factors on COD concentrations. Further improvement of COD prediction requires the integration of additional variables, such as pH, temperature, conductivity, and dissolved oxygen. Model validation and recalibration are also recommended before extrapolation to other wastewater treatment stations.