Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have limitations in responding to the impact of soil moisture changes and runoff characteristics. To address these limitations, a method was developed for selecting predictor variables that utilize the accumulation of rainfall at various time intervals to represent soil moisture, the changes in the runoff coefficient, and runoff characteristics.