Artificial Neural Network-Based Prediction of Water Quality for Pollution Assessment in Freshwater Ecosystems

Abstract

This study proposes an innovative, intelligent, and scalable approach to water pollution monitoring by utilizing an Artificial Neural Network (ANN) to predict the Water Quality Index (WQI), thereby overcoming the limitations of traditional, labour-intensive monitoring methods. The analysis was conducted on River A in Johore, Malaysia, a representative freshwater system experiencing significant urban and industrial pressure. The dataset, comprising 90 samples collected across a nine-weeks sampling period, included measurements for eight key physicochemical parameters: pH, temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia, total suspended solids (TSS), and turbidity. The developed ANN model demonstrated superior predictive capability, achieving a high coefficient of determination (R2) of 0.9902 on the training dataset and 0.9956 on the testing dataset, confirming its effectiveness in capturing the complex, nonlinear pollution dynamics. Application of the model revealed the river's overall status to be Class IV (Polluted) based on the Malaysian Water Quality Standards. Furthermore, the study identified that acute pollution loading, evidenced by excessive COD and Class V Ammonia Nitrogen (NH3-N) levels (up to 9.31 mg/L), is primarily driven by heavy rainfall events mobilizing surface runoff and contamination from nearby industrial activities. This validated ANN framework offers a cost-effective and reliable tool for real-time pollution forecasting, empowering decision-makers in the sustainable management and ecological restoration of polluted river basins.

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