<![CDATA[This research developed a rainfall-runoff model to predict inflow at the Bili-Bili Dam and to evaluate the model's accuracy. The Soil Conservation Service Curve Number (SCS-CN) method, implemented using HEC-HMS software, was employed to construct the rainfall-runoff model. Model accuracy was assessed using three statistical indices: Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Coefficient of Determination (R²), by comparing the predicted inflow with measured inflow data. Climatological data (rainfall) and topographic data served as model inputs, while measured inflow data provided the benchmark for validation. Model simulations for the January 14-17, 2024, flood period yielded a peak inflow of 673.6 m³/s at the Bili-Bili Dam on January 15, 2024. The model's temporal prediction aligned with the measured data, although the predicted inflow was slightly lower than the observed values. Statistical evaluation revealed good model performance, indicated by an NSE of 0.793, a PBIAS of -1.86%, and an R² of 0.81. The high NSE value signifies good agreement between predicted and measured data, while the small negative PBIAS suggests a tendency for the model to slightly underestimate inflow. The high R² value (0.81) indicates that 81% of the inflow variability is explained by the model. Despite the model's satisfactory performance, further calibration and refinement are recommended to enhance predictive accuracy, particularly in complex hydrological conditions. This research contributes to improved inflow prediction capabilities at the Bili-Bili Dam, crucial for effective water resource planning and management in the region. The findings support more informed decision-making in flood management and mitigation within the Bili-Bili watershed..]]>
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