Rainfall forecast model based on the tabnet model

Abstract

To further reduce the error rate of rainfall prediction, we used a new machine learning model for rainfall prediction and new feature engineering methods, and combined the satellite sys-tem's method of observing rainfall with the machine learning prediction. Based on multivariate correlations among meteorological information, this study proposes a rainfall forecast model based on the Attentive Interpretable Tabular Learning neural network (TabNet). This study used self-super-vised learning to help the TabNet model speed up convergence and maintain stability. We also used feature engineering methods to alleviate the uncertainty caused by seasonal changes in rainfall fore-casts. The experiment used 5 years of meteorological data from 26 stations in the Beijing–Tianjin– Hebei region of China to verify the proposed rainfall forecast model. The comparative experiment proved that our proposed method improves the performance of the model, and that the basic model used is also superior to other traditional models. This research provides a high-performance method for rainfall prediction and provides a reference for similar data-mining tasks.