Spatial prediction using random forest spatial interpolation with sample augmentation: a case study for precipitation mapping

Spatial prediction(SP) based on machine learning(ML) has been applied to soil water quality, air quality, marine environment, etc. However, there are still deficiencies in dealing with the problem of small samples. Normally, ML requires large amounts of training samples to prevent underfitting. And the data augmentation(DA) methods of mixup and synthetic minority over-sampling technique(SMOTE) ignore the similarity of geographic information. Therefore, this paper proposes a modified upsampling method and combines it with the random forest spatial interpolation(RFSI) to deal with the small sample problem in geographical space. The modified upsampling is mainly reflected in the following two aspects. Firstly, in the process of selecting the nearest points, it is to select points with similar geographic information in some aspects of the category after classification. Secondly, the selected difference is the difference of each category. In order to verify the effectiveness of the proposed method, we use daily precipitation data for January 2018 in Chongqing. The experimental results show that the combination of the modified upsampling method and RFSI effectively improves the accuracy of SP.