Reservoir inflow prediction using machine learning techniques

Accurate reservoir inflow prediction is vital for effective water resource management in regions like the Upper Indus Basin (UIB), characterized by complex hydrological dynamics and increasing climate variability. This study evaluates the performance of Random Forest and KNN algorithms in predicting inflows for the Tarbela Dam, a critical infrastructure in Pakistan. Using historical data on precipitation, temperature, and inflow, the analysis reveals that Random Forest consistently outperforms KNN by capturing the variability and non-linear relationships inherent in UIB's hydrology. Random Forest demonstrated superior accuracy in tracking peaks and troughs, whereas KNN smoothed fluctuations, resulting in underestimations and overestimations, particularly during periods of high inflow. Evaluation metrics, including R², MAE, RMSE, and MBE, confirmed Random Forest's better predictive capabilities, while box plot analysis highlighted its effectiveness in capturing variability and extreme events in inflow data compared to KNN. These results emphasize the importance of accurate inflow predictions for reservoir operation, flood control, and drought mitigation.