Assessing the Performance of the Extreme Gradient Boosting Machine Model in Forecasting Precipitation Depth Aiming at Improved Accuracy for Precipitation Estimation in Data-Scarce Regions

Haddadi, Amirhossein; Danesh Yazdi, Mohammad

doi:10.22034/iwrr.2025.543507.2944

Assessing the Performance of the Extreme Gradient Boosting Machine Model in Forecasting Precipitation Depth Aiming at Improved Accuracy for Precipitation Estimation in Data-Scarce Regions

Document Type : Original Article

Authors

Amirhossein Haddadi ¹

Mohammad Danesh Yazdi ²

¹ Sharif University of Technology,

² Department of Civil Engineering, Sharif University of Technology

10.22034/iwrr.2025.543507.2944

Abstract

Estimating precipitation height in data-scarce regions is important for its wide application in water resource management-related topics. In this study, the performance of the Extreme Gradient Boosting (XGBoost) model was evaluated for predicting precipitation depth at short-term forecasting horizons, ranging from daily to 7-day averaged precipitation, in the Karkheh River Basin. For this purpose, three-hourly data on precipitation, air temperature, and relative humidity recorded at synoptic stations during the period from January 2001 to February 2024 were used. Then, data preprocessing steps were carried out, including completing missing data recorded by stations and removing outliers. Additionally, to consider the effects of precipitation, relative humidity, and air temperature from previous days on the current day’s precipitation prediction, time lags from one to seven past days were applied to the data and prepared as inputs to the machine learning model in seven different scenarios. Test section results showed that the prediction accuracy of the model increased with the use of more historical information; such that in the seven-day scenario, the model demonstrated more accurate precipitation time prediction with R² = 0.93, RMSE = 0.41 mm, and MAE = 0.19 mm compared to the one-day scenario, which had indices of R² = 0.46, RMSE = 1.13 mm, and MAE = 0.61 mm.

Keywords

Precipitation prediction

Machine learning

XGBoost

Karkheh River Basin

Subjects