Drought monitoring and forecasting are essential for efficient water resources management. The present research aims to provide a reliable prediction of the effective Reconnaissance Drought Index (eRDI) based on seven evaporation stations in the southern Baluchestan sub-basin of Iran. To achieve this purpose, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) machine learning methods are used and combined with the marine predator optimization algorithm (MPA) to enhance efficiency. Drought monitoring and forecasting have been performed on time scales of 1-, 3-, and 6-months intervals. The results demonstrated the superiority of the ANFIS-MPA algorithm over the SVR-MPA and ANN-MPA approaches. In addition, as the time scale increased, the accuracy of all models improved. The best results were for the eRDI 6-month at Kajdar Sarbaz station by ANFIS-MPA (MAE = 0.33, NSE = 0.83, R = 0.99), SVR-MPA (MAE = 0.36, NSE = 0.78, R = 0.85) and ANN-MPA (MAE = 0.37, NSE = 0.72, R = 0.83).

中文翻译：

---

使用先进的混合机器学习预测炎热干燥气候下的农业干旱指数

干旱监测和预报对于有效的水资源管理至关重要。本研究旨在基于伊朗俾路支斯坦次流域南部的七个蒸发站，对有效勘测干旱指数（eRDI）提供可靠的预测。为了实现这一目的，采用人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）和支持向量回归（SVR）机器学习方法，并与海洋捕食者优化算法（MPA）相结合来提高效率。干旱监测和预报的时间间隔为1个月、3个月和6个月。结果证明了 ANFIS-MPA 算法相对于 SVR-MPA 和 ANN-MPA 方法的优越性。此外，随着时间尺度的增加，所有模型的准确性都提高了。最佳结果是由 ANFIS-MPA (MAE = 0.33, NSE = 0.83, R = 0.99)、SVR-MPA (MAE = 0.36, NSE = 0.78, R = 0.85) 和 ANN 在 Kajdar Sarbaz 站进行的 6 个月 eRDI -MPA（MAE = 0.37，NSE = 0.72，R = 0.83）。