A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable is great importance. Therefore, purpose this study was develop a multi-source data fusion method improve accuracy products. study, from 14 existing products, digital elevation model (DEM), land surface temperature (LST) soil water index (SWI) recorded at 256 gauge stations in Saudi Arabia were used. first step, assessed. second importance degree various independent variables, such as interpolation maps obtained stations, elevation, LST SWI improving modelling, evaluated. Finally, produce product with higher accuracy, information variables combined using machine learning algorithm. Random forest regression 150 trees used The highest lowest production based on characteristics, respectively. properties including SWI, DEM 65%, 22% 13%, IMERGFinal (9.7), TRMM3B43 (10.6), PRECL (11.5), GSMaP-Gauge (12.5), CHIRPS (13.0 mm/mo) had RMSE values. KGE values these estimation 0.56, 0.48, 0.52, 0.44 0.37, 6.6 mm/mo 0.75, respectively, which indicated compared results showed that different improved estimation.