Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions.

干旱是一种复杂的自然现象，因此，对干旱的精确预测是衡量对农业，生态系统，水文学和水资源的不利影响的有效缓解工具。这项研究的目的是探索结合两个元启发式算法（即灰狼优化器（GWO）和斑点鬣狗优化器（SHO））的支持向量回归（SVR）的潜在能力，以通过以下方法预测气象干旱（MD）：利用EDI（有效干旱指数）。为了这个目标，在北阿坎德邦邦（印度）的库马恩和加尔瓦尔地区建立了两个混合的SVR-GWO模型和SVR-SHO模型。通过使用每月降雨数据系列来校准和验证高级混合SVR模型，在两个研究区域中计算了EDI。利用自相关函数（ACF）和部分ACF（PACF）来确定EDI预测的最佳输入（事前EDI）。通过使用统计指标和通过图形检查，将混合SVR模型产生的结果与计算（观察到的）值进行比较。结果比较表明，对于位于库蒙和加尔瓦尔地区的所有研究站，混合SVR-GWO模型均优于SVR-SHO模型。此外，结果还强调了元启发式算法（即GWO和SHO）在研究区域的气象干旱预测中具有更好的适用性，至上性和收敛性。通过使用统计指标和通过图形检查，将混合SVR模型产生的结果与计算（观察到的）值进行比较。结果比较表明，对于位于库蒙和加尔瓦尔地区的所有研究站，混合SVR-GWO模型的性能均优于SVR-SHO模型。此外，结果还强调了元启发式算法（即GWO和SHO）在研究区域的气象干旱预测中具有更好的适用性，至上性和收敛性。通过使用统计指标和通过图形检查，将混合SVR模型产生的结果与计算（观察到的）值进行比较。结果比较表明，对于位于库蒙和加尔瓦尔地区的所有研究站，混合SVR-GWO模型的性能均优于SVR-SHO模型。此外，结果还强调了元启发式算法（即GWO和SHO）在研究区域的气象干旱预测中具有更好的适用性，至上性和收敛性。