Assessment of the Support Vector Regression and Random Forest Algorithms in the Bias Correction Process on Temperatures

Abstract

Climate information can be obtained from General circulation models (GCMs). However, this model has poor resolution, so it is necessary to do bias correction to overcome this problem. This study carried out a bias correction process using the Support Vector Regression (SVR) and Random Forest (RF) approaches. Bias correction is carried out for temperature in Indonesia using the BNU-ESM and MERRA-2 climate models, which act as observational data. The results show that the RF method (RMSE: 0.334; Correlation: 0.694; Standard Deviation: 0.582) is better than SVR (RMSE: 0.341; Correlation: 0.675; Standard Deviation: 0.588) in performing bias correction.