Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data

Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, not accurate the sense that do agree ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. This correction takes form of a regression problem, which measurements have role dependent variable and data predictor variables, together topography factors (e.g., elevation). Most studies this kind involve limited number algorithms, conducted small region time period. Thus, results obtained through local importance provide more general guidance best practices. To generalizable contribute delivery practices, we here compare eight state-of-the-art algorithms correcting entire contiguous United States 15-year We use monthly from PERSIANN (Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks) gridded dataset, earth-observed Global Historical Climatology Network database, version 2 (GHCNm). The suggest extreme gradient boosting (XGBoost) random forests most terms squared error scoring function. remaining can be ordered follows worst: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), machines (gbm), (MARS), linear regression.