Drought is a phenomenon that occurs slowly, affecting surface water and groundwater resources, which can reduce the water supply, worsen water quality and deteriorate agricultural products, and affect economic and political activities. Urmia Lake basin has been exposed to severe water stress in recent years. Therefore, in this study, drought monitoring in Tabriz synoptic station as one of the most important stations in Urmia Lake basin in the short-term, mid-term, and long-term steps of 53 years period was investigated using the Standard Precipitation and Evapotranspiration Index (SPEI). Then, the prediction of the drought was investigated using a hybrid model of the genetic algorithm (GA) and autoregressive integrated moving average model (ARIMA) approaches. The results showed that there were three long periods of drought related to 1961–1963, 1986–1992, and 1997–2009 during the statistical period. In the prediction section, the results showed that based on the Brock-Dechert-Scheinkman (BDS) test, in all SPEI time scales, the time series has predictability. Also, the prediction accuracy of the GA-ARIMA model has a direct correlation with the SPEI time scale. So that in the test section, the determination coefficient in the 1-month time scale (SPEI1) has increased from 0.34 to 0.93 in the 48-month time scale (SPEI48).

干旱是一种缓慢发生的现象，会影响地表水和地下水资源，从而减少供水，恶化水质和恶化农产品，并影响经济和政治活动。近年来，乌尔米亚湖流域面临严重的缺水压力。因此，在这项研究中，使用标准降水和蒸散量研究了大不里士天气站（在Urmia湖流域最重要的站之一）在53年的短期，中期和长期步骤中的干旱监测。索引（SPEI）。然后，使用遗传算法（GA）和自回归综合移动平均模型（ARIMA）方法的混合模型研究了干旱的预测。结果表明，与1961–1963年有3个长期干旱，在统计期内是1986-1992年和1997-2009年。在预测部分，结果表明，基于Brock-Dechert-Scheinkman（BDS）检验，在所有SPEI时间范围内，时间序列均具有可预测性。而且，GA-ARIMA模型的预测准确性与SPEI时标具有直接相关性。因此，在测试部分中，1个月时标（SPEI1）的确定系数从48个月时标（SPEI48）的0.34增加到了0.93。