Enhancing rainfall estimation accuracy with machine learning, cloud masking, and multi-source data: A case study of four coastal provinces in central Vietnam

Abstract

This study develops the LGBM-3SC-CF product, a machine learning framework that integrates multi-source data (Himawari-8, ERA5, ASTERDEM, and rain gauge) to significantly improve rainfall classification and estimation accuracy for four coastal provinces in Central Vietnam. Methodologically, our core contribution lies in the proposed 3-stage classification architecture combined with a novel cloud masking technique. To address the severe class imbalance inherent in rainfall datasets, we further employed an effective data balancing technique based on rainfall intensity distribution alongside feature selection based on cloud- and rain-forming factors. The performance of the proposed rainfall product was compared with four existing regional rainfall products: IMERG Final Run V07, IMERG Early Run V06, GSMaP_MVK_Gauge V07, and PERSIANN-CCS. The LGBM-3SC-CF achieved the highest performance, with a Critical Success Index (CSI) of 0.55 and a Probability of Detection (POD) of 0.74, and achieved the Correlation Coefficient (CC) of 0.47 and the Modified Kling-Gupta Efficiency (mKGE) of 0.47. Furthermore, it obtained the lowest error metrics, with a Mean Absolute Error (MAE) of 2.66 mm/h and a Root Mean Squared Error (RMSE) of 5.48 mm/h. This study not only establishes a robust machine learning framework but also provides the essential methodological foundation for developing near real-time rainfall estimation models through the seamless substitution of the atmospheric reanalysis features with near real-time meteorological features.