Application of data driven modeling in assessing the effect of influent variability and weather changes on the performance of Tororo wastewater stabilization ponds

Abstract

This study investigated the Tororo Wastewater Treatment Plant’s stabilization ponds under varying climatic conditions and increasing influent loads, aiming to guide sustainable water management in alignment with Uganda's Vision 2040 and SDGs 6, 11, and 13. Methodologically, historical data (2014–2024) covering influent rates, wastewater characteristics, and key climate variables were analyzed using descriptive statistics, Principal Component Analysis (PCA), Cluster Analysis (CA), and regression analyses to characterize pond performance and identify dominant influencing factors. Subsequently, three data-driven predictive models Multivariate Regression, Artificial Neural Networks (ANN), and a Hybrid GRU-LSTM Recurrent Neural Network (RNN) were developed and tested using normalized and partitioned datasets. An LSTM-based forecasting model was also developed to project future influent parameters. Key results showed that Facultative Ponds 1 and 2 frequently experienced negative TSS removal efficiency and moderate BOD/COD reductions (19–26%). In contrast, the Maturation Pond demonstrated more stable TSS reduction (around 26.9%) and slightly higher BOD removal (up to 37.4%), but struggled with ammonia removal efficiency. All ponds achieved consistently high fecal coliform attenuation (≥ 89%). The Hybrid GRU-LSTM model delivered superior predictive accuracy for effluent parameters (R² up to 0.97 for BOD, COD, TSS), outperforming other models by effectively capturing temporal dependencies. Feeding forecasted influent parameters into this Hybrid GRU-LSTM model achieved robust accuracy for future effluent predictions (overall R² ≈ 0.81). These findings highlight that data-driven forecasting, coupled with targeted operational interventions (e.g., optimized sludge management, improved pond hydraulics), can enhance stabilization pond resilience. This approach ensures more reliable wastewater treatment and supports sustainable development goals in rapidly urbanizing settings.