Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM’s parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM’s accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.

帕尔默干旱严重度指数（PDSI）被认为是一种强有力的农业干旱指数，因为它考虑了土壤中的水分平衡条件。它已被广泛用作监测农业干旱的参考指标。在这项研究中，为9个天气观测站计算了PDSI时间序列，以监测位于伊朗Zagros山区的半干旱地区的农业干旱。自回归移动平均值（ARMA）被用作随机模型，而径向基函数神经网络（RBFNN）和支持向量机（SVM）被用作基于机器学习（ML）的技术。根据PDSI的时间序列分析，在最干旱的月份中，PDSI干旱事件最多的是正常干旱和轻度干旱条件。作为一项创新，本研究中使用蜻蜓算法（DA）来优化SVM的参数，称为混合SVM-DA模型。值得一提的是，混合SVM-DA在水文学研究中首次被开发为一种元创新模型。新颖的混合SVM-DA范例可以将SVM的准确度提高到29％，从而可以预测PDSI，因此被认为是更好的模型。获得该模型的最佳统计信息是：均方根误差（RMSE）= 0.817，归一化RMSE（NRMSE）= 0.097，Wilmott指数（WI）= 0.940，R = 0.889。通过新的SVM-DA模型进行的PDSI预测的平均绝对误差值在早期干旱下为0.6以下，在中度和中度干旱下为0.7以下。一般而言，严重干旱和极端干旱的误差值比其他类别大。但是，在大多数情况下，混合SVM-DA是性能最佳的模型。值得一提的是，混合SVM-DA在水文学研究中首次被开发为一种元创新模型。新颖的混合SVM-DA范例可以将SVM的准确度提高到29％，从而可以预测PDSI，因此被认为是更好的模型。获得该模型的最佳统计信息是：均方根误差（RMSE）= 0.817，归一化RMSE（NRMSE）= 0.097，Wilmott指数（WI）= 0.940，R = 0.889。通过新的SVM-DA模型进行的PDSI预测的平均绝对误差值在早期干旱下为0.6以下，在中度和中度干旱下为0.7以下。一般而言，严重干旱和极端干旱的误差值比其他类别大。但是，在大多数情况下，混合SVM-DA是性能最佳的模型。值得一提的是，混合SVM-DA在水文学研究中首次被开发为一种元创新模型。新颖的混合SVM-DA范例可以将SVM的准确度提高到29％，从而可以预测PDSI，因此被认为是更好的模型。获得该模型的最佳统计信息是：均方根误差（RMSE）= 0.817，归一化RMSE（NRMSE）= 0.097，Wilmott指数（WI）= 0.940，R = 0.889。通过新的SVM-DA模型进行的PDSI预测的平均绝对误差值在初期干旱下为0.6以下，在轻度和中度干旱下为0.7以下。一般而言，严重干旱和极端干旱的误差值比其他类别大。但是，在大多数情况下，混合SVM-DA是性能最佳的模型。