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

摘要：

为构建准确的干旱预测模型，选择具有物理意义的预测因子和采用高效的预测方法至关重要。与传统预测技术相比，机器学习算法因其高效性和可靠性，在气候预测中被广泛应用。本文基于1960—2022年湖北省70个国家气象站逐月气象要素数据，以及国家气候中心与美国国家海洋和大气管理局提供的大气环流和海温指数，采用标准化降水蒸散指数判断是否干旱作为目标变量，运用特征选择方法筛选出11个指数作为输入变量。在此基础上，分别使用分类回归树和随机森林两种机器学习算法，构建了湖北省夏季干旱预测模型。随机选取47 a数据作为训练集，并利用剩余16 a数据作为测试集，对预测结果进行检验。结果表明，分类回归树和随机森林模型对干旱是否发生的预测准确率分别为88%和81%。此外，两种算法建模时均将亚洲纬向环流指数列为最重要的变量，表明该指数在湖北省夏季干旱预测中具有关键作用。通过构建两种机器学习算法预测模型，为湖北省夏季干旱预测提供了客观有效的新思路，对湖北省防旱减灾具有重要意义。

关键词: 湖北省, 夏季, 机器学习, 干旱预测, 模型构建