IMPROVING PRECIPITATION ESTIMATION ACCURACY FOR THE CENTRAL VIETNAM REGION USING THE XGBOOST MODEL WITH MULTI-SOURCE DATA

Abstract

This paper presents a novel approach to enhancing the accuracy of precipitation estimation in Central Vietnam using the Extreme Gradient Boosting (XGBoost) machine learning model. The proposed method integrates multi-source data, combining satellite imagery from Himawari-8, atmospheric reanalysis from ERA-5, and digital elevation models from ASTER DEM to train the model. Rain gauge data from 175 stations across the region are used as target labels for validation. The proposed model achieved a CSI of 0.45, a POD of 0.75, and an RMSE of 4.53, with improvements of 11.11% to 86.67%, 28% to 93.33%, and 16.99% to 51.87%, respectively, compared to other precipitation products such as IMERG-Final Run, GSMaP_MVK, FengYun 4A, and PERSIANN-CCS. Detailed rainfall maps generated by the proposed model were compared with radar imagery during rainfall events, demonstrating a high degree of similarity. Furthermore, this approach serves as the basis for running near-real-time rainfall estimation models for the region of Vietnam.