Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.

Abbreviations: ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; ANN: artificial neural network; BS-SVR: boosted-support Vector Regression; CC: correlation coefficient; ELM: extreme learning machine; GEP: gene Expression Programming; GP: genetic Programming; GPR: Gaussian process regression; KNN: k-nearest neighbor; LSSVM: least squares Support Vector Machine; LSSVR: least support vector regression; MAE: mean absolute error; MARS: multivariate adaptive regression splines; MLP: multilayer perceptron; MLR: multiple linear regression; MT: M5 model tree; P: precipitation; PDSI: palmer drought severity index; PET: potential evapotranspiration; RAE: relative absolute error; RMSE: root mean square error; RVM: relevance vector machine; SAR: sodium absorption index; SDR: standard deviation reduction; SPEI: standardized precipitation evapotranspiration index; SPI: standardized precipitation index; SSI: standardized streamflow index; SVM: support vector machine; SVR: support vector regression; WAANN: Wavelet-ARIMA-ANN; WANFIS: Wavelet-Adaptive Neuro-Fuzzy Inference System; WN: wavelet network

水文干旱的特征是根据其持续时间，严重程度和强度来确定。在最关键的因素中，降水，蒸散和径流对于干旱建模至关重要。在这项研究中，使用支持向量回归（SVR），基因表达程序设计（GEP）对三个干旱指标进行了建模，即标准降水指数（SPI），标准水流指数（SSI）和标准降水蒸散指数（SPEI）。 ，以及M5模型树（MT）。结果表明，SPI提供了更高的精度。此外，MT模型在CC值为0.8195，RMSE为0.8186的SSI预测中表现更好。

缩略语：ANFIS：自适应神经模糊推理系统；ANN：人工神经网络；ANN：人工神经网络；BS-SVR：增强支持向量回归；CC：相关系数；ELM：极限学习机；GEP：基因表达编程；GP：基因编程；GPR：高斯过程回归；KNN：k最近邻居；LSSVM：最小二乘支持向量机；LSSVR：最小支持向量回归；MAE：平均绝对误差；MARS：多元自适应回归样条；MLP：多层感知器；MLR：多元线性回归；MT：M5模型树；P：降水；PDSI：帕尔默干旱严重性指数；PET：潜在的蒸散量；RAE：相对绝对误差；RMSE：均方根误差；RVM：相关向量机；SAR：钠吸收指数；SDR：减少标准偏差；SPEI：标准化降水蒸散指数；SPI：标准化降水指数；SSI：标准化流量指数；SVM：支持向量机；SVR：支持向量回归；WAANN：Wavelet-ARIMA-ANN；WANFIS：小波自适应神经模糊推理系统；WN：小波网络