Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and drought index calculation is critical in the tracking of drought. The Palmer Drought Severity Index is one of the most widely used methods in drought calculation. The drought calculation according to Palmer is a time-consuming process. Such a troublesome can be made easier using advanced machine learning algorithms. Therefore, in this study, the advanced machine learning algorithms (LR, ANN, SVM, and DT) were employed to calculate and estimate the Palmer drought Z-index values from the meteorological data. Palmer Z-index values, which will be used as training data in the classification process, were obtained through a special-purpose software adopting the classical procedure. This special-purpose software was developed within the scope of the study. According to the classification results, the best R-value (0.98) was obtained in the ANN method. The correlation coefficient was 0.98, Mean Squared Error was 0.40, and Root Mean Squared Error was 0.56 in this success. Consequently, the findings showed that drought calculation and prediction according to the Palmer Index could be successfully carried out with advanced machine learning algorithms.

Graphical Abstract

中文翻译：

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使用先进的机器学习算法，可以根据气象数据估算和轻松计算Palmer干旱严重性指数。

干旱已成为全球最严重的环境问题之一，对生态，经济和社会可持续发展产生了严重影响。干旱监测过程对干旱风险的管理至关重要，而干旱指数的计算对于干旱的追踪至关重要。帕尔默干旱严重度指数是干旱计算中使用最广泛的方法之一。根据Palmer的计算干旱是一个耗时的过程。使用高级机器学习算法可以使这种麻烦变得更加容易。因此，在这项研究中，采用了先进的机器学习算法（LR，ANN，SVM和DT）从气象数据中计算和估算Palmer干旱Z指数值。帕尔默Z索引值，将在分类过程中用作训练数据，是通过采用经典程序的专用软件获得的。该专用软件是在研究范围内开发的。根据分类结果，在ANN方法中获得最佳R值（0.98）。在此成功中，相关系数为0.98，均方误差为0.40，均方根误差为0.56。因此，研究结果表明，可以使用先进的机器学习算法成功地执行根据Palmer指数进行的干旱计算和预测。

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