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

Abstract

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