关键词: 湖北省, 夏季, 机器学习, 干旱预测, 模型构建

Abstract:

In order to construct an accurate drought prediction model, it is very important to select predictors with physical significance and adopt efficient prediction methods. Compared to the traditional prediction methods, more efficient and reliable machine learning algorithms have been more widely used in climate prediction. This study is based on the monthly meteorological element data of 70 national meteorological stations in Hubei Province from 1960 to 2022, as well as the atmospheric circulation and sea temperature indices provided by the National Climate Center and the National Oceanic and Atmospheric Administration (NOAA). The standardized precipitation evapotranspiration index was used to determine drought occurrence as the target variable, and 11 indices were selected as input variables using feature selection methods. On this basis, two machine learning algorithms, classification and regression tree (CART) and random forest (RF), were used to construct summer drought prediction models of Hubei Province. The 47 years data were randomly selected as the training set, while the remaining 16 years data were used as the test set to evaluate the prediction performance. The results show that the prediction accuracy of the CART and RF models for drought was 88% and 81%, respectively. Additionally, both algorithms identified the Asian zonal circulation index as the most important variable in their models, indicating that this index is crucial for predicting summer droughts in Hubei Province. By constructing these two machine learning algorithm prediction models, this study provides an objective and effective new approaches for summer drought prediction in Hubei Province, which will provide scientific information for drought prevention and mitigation in the region.

Key words: Hubei Province, summer, machine learning, drought prediction, model building