In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972–2000), near future (2020–2040) and far future (2070–2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE) = 0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).

在本研究中，首次将元启发式算法，分解和机器学习相结合的新框架用于气候变化条件下的洪水频率分析以及HadCM3（A2和B2场景），CGCM3（A2和A1B场景）的应用）和CanESM2（RCP2.6，RCP4.5和RCP8.5方案）的全球气候模型（GCM）。在提出的框架中，将多元自适应回归样条（MARS）和M5模型树用于降水分类（干日和干日），考虑使用鲸鱼优化算法（WOA）来训练最小二乘支持向量机（LSSVM），小波变换（WT）用于分解降水和温度，执行LSSVM-WOA，LSSVM，K最近邻（KNN）和人工神经网络（ANN）来缩减降水和温度，在当前时期（1972–2000），近期（2020–2040）和遥远的未来（2070–2100）下模拟排放和排放。对数正态分布用于洪水频率分析。此外，采用方差分析和模糊方法进行不确定性分析。伊朗西南部的Karun3盆地被认为是一个案例研究。结果表明，MARS的性能优于M5模型树。在缩减规模方面，ANN和LSSVM_WOA略胜于其他机器学习算法。模拟放电的结果显示了LSSVM_WOA_WT算法的优越性（纳什-苏特克利夫效率（NSE）= 0.911）。洪水频率分析的结果表明，在不久的将来，除CanESM2 RCP2.6情景外，所有情景的200年排放量都会减少。在不久的将来 从ANOVA不确定性分析中可以明显看出，水文模型是最重要的不确定性来源之一。基于模糊不确定性分析，HadCM3模型在较高的回报期内具有较低的不确定性（在1000年的回报期内比其他模型低多达60％）。