Hydrological drought is an environmental event that affects surface water resources such as surface runoff and reservoir levels and its prediction, can help the water managers to be aware of the future status of the region. This study aims to predict and evaluate hydrological drought in the southwestern margin of the Caspian Sea. For this, Streamflow Drought Index (SDI) is used as a multi-scalar hydrological drought indicator and calculated in time windows of 1, 3, 6, 9 and 12-month. In this study, time series stochastic models were used for the first time to predict SDI and compared with two black-box machine learning (ML) methods including Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH). The used data belongs to two rivers named Khalkaei and Pasikhan in Guilan province, and the period of 1986–2015 on a monthly scale. Autocorrelation and Partial Autocorrelation Functions (ACF and PACF) was used for selecting inputs among the series’ monthly time lags. The results showed that there are seasonal trends in SDI’s 1, 3, 6 and 9-month time windows; therefore, these time windows have better adaptability with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. But there is no seasonal trend in the 12-month time window (SDI₁₂) series and the non-seasonal Autoregressive Moving Average (ARMA) model was found as the best choice for this time window. Among the models, the MLs (GMDH and ANFIS) had approximately similar prediction accuracies. Comparing the models indicate that the linear stochastic models in addition to the simplicity of use were significantly more accurate than the complex non-linear ML models. The current results suggest using the stochastic models for hydrological drought forecasting, in catchment areas.

水文干旱是一种环境事件，会影响地表水资源，例如地表径流和水库水位及其预测，可以帮助水管理人员了解该地区的未来状况。这项研究旨在预测和评估里海西南缘的水文干旱。为此，将河流干旱指数（SDI）用作多尺度水文干旱指标，并在1、3、6、9和12个月的时间窗口中进行计算。在这项研究中，首次使用时间序列随机模型预测SDI，并将其与包括自适应神经模糊推理系统（ANFIS）和数据处理组方法（GMDH）在内的两种黑盒机器学习（ML）方法进行了比较。所使用的数据属于桂兰省的两条河流，分别为Khalkaei和Pasikhan，以及每月1986-2015年的期间。自相关和部分自相关函数（ACF和PACF）用于在系列每月时滞中选择输入。结果表明，SDI的1、3、6和9个月时间窗口存在季节性趋势；因此，这些时间窗口与季节性自回归综合移动平均线（SARIMA）模型具有更好的适应性。但是12个月的时间范围内没有季节性趋势（SDI₁₂）系列和非季节性自回归移动平均线（ARMA）模型被认为是此时间窗口的最佳选择。在这些模型中，ML（GMDH和ANFIS）具有近似相似的预测精度。对模型进行比较表明，除了使用简单之外，线性随机模型比复杂的非线性ML模型精确得多。目前的结果表明，在流域使用随机模型进行水文干旱预报。