Water Quality Prediction in Inland Lakes Using the Streeter Phelps Dissolved Oxygen Algorithm

Maintaining optimal water quality in inland lakes is important for ecological balance, human consumption, and recreational use. This study employs the Streeter-Phelps Dissolved Oxygen (DO) algorithm to predict the dynamics of water quality due to the lack of oxygen and recovery of water from the bottom of organic pollution sources. The algorithm consists of major parameters such as biochemical oxygen demand (BOD), deoxygenation Rate (K₁), and Rate (K₂), which are used to estimate the spatial and temporal distribution of dissolved oxygen concentrations. Field data of selected inland lakes was integrated into the model to follow the pollution scenarios of the real world. The results reveal oxygen relaxed curve behaviour under different environmental conditions, enabling the identification of important areas for hypoxia. Verification against empirical water quality measurements displays the reliability of the model in the forecast of DO levels and assesses pollution load effects. This modelling structure provides a cost-effective and scientifically sound approach for lake management authorities to evaluate pollution mitigation strategies and ensure the stability of aquatic ecosystems.