One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

流域管理和水资源可持续利用的最重要基础之一是水文参数的预测。在这项研究中，使用支持向量机（SVM），结合小波变换的支持向量机（W-SVM），带有外生变量的自回归移动平均值（ARMAX）模型和自回归综合移动平均值（ARIMA）模型来预测月度值沉淀，排放和蒸发。为此，使用了哈密丹集水区在25年期间（1991年至2015年）的月雨量，水文和蒸发量测站的每月时间序列。在此统计期间内，分别使用了17年（1991-2007），4年（2008-2011）和4年（2012-2015）进行了模型的训练，校准和验证。结果表明，ARIMA，SVM，ARMAX和W-SVM在月降水量预测中排名第一至第四，而SVM，ARIMA，ARMAX和W-SVM在月排放量和月蒸发量预测中排名第一至第四。 。可以说，SVM的可调参数比其他型号少。因此，该模型能够更轻松，更轻松地预测水文变化，因此，该模型优于其他方法。