Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction.

使用现有的方法来训练数值（非分类）预测模型可能会导致对干旱的错误分类。因此，制定基于干旱类别的措施至关重要。此外，现有的固定干旱类别阈值需要改进。本研究的目的是开发基于改进的干旱分类方法的基于类别的评分支持向量回归 (CBS-SVR) 模型，以克服干旱预测中的错误分类。为了得出干旱分类的可变阈值水平，K均值（KM）和高斯混合（GM）聚类与传统的干旱分类进行了比较。对于干旱预测，CBS-SVR 使用最佳分类方法进行。新的干旱模型应用于美国北部盆地（RRB）的红河。在模型训练和测试中，选择降水、温度和实际蒸散作为预测变量，目标变量包括多变量干旱指数，以及双变量和单变量标准化干旱指数。结果表明，干旱分类方法、可变阈值水平和干旱指数类型是影响干旱预测准确性的主要因素。