One method for estimating floods in areas lacking statistical data is the use of regional frequency analysis based on machine learning. In this study, statistical and clustering-based approaches were evaluated for flood estimation in the Karkheh watershed. The hydrological homogeneity of the obtained zones was then assessed using linear moments and heterogeneity adjustment methods proposed by Hosking and Wallis. Then, the ZDIST statistic was used to calculate the three-parameter distributions for stations within each hydrologically homogeneous cluster. These parameters were computed using linear moments, and floods with different return periods at each station were estimated using regional relationships. The results indicated the creation of two clusters in this area, with five stations in cluster one and 11 stations in cluster two. The statistical homogeneity values for clusters one and two were calculated as 0.33 and 0.17, respectively, indicating the homogeneity of each region. Generalized Pearson type III and generalized extreme value distributions were selected as the best regional distributions for clusters 1 and 2, respectively. The results also showed that floods could be estimated for return periods of 2, 5, 25 years, and more. The highest estimated flood is predicted at the Jelugir-e Majin station, where the flood with a 2-year return period reaches 1034 m³ s⁻¹. This increases to 5360 m³ s⁻¹ for a 100-year return period. The approach presented in this study is recommended for similar regions lacking complete information.

在缺乏统计数据的地区估计洪水的一种方法是使用基于机器学习的区域频率分析。在这项研究中，评估了 Karkheh 流域洪水估计的统计和基于聚类的方法。然后使用 Hosking 和 Wallis 提出的线性矩和非均质性调整方法评估所获得区域的水文均匀性。然后，使用 ZDIST 统计量计算每个水文均匀集群内站点的三参数分布。这些参数是使用线性矩计算的，并且使用区域关系估计每个站点具有不同重现期的洪水。结果表明，在该区域创建了两个集群，集群 1 中有 5 个站点，集群 2 有 11 个站点。聚类 1 和 2 的统计同质性值分别计算为 0.33 和 0.17，表明每个区域的同质性。广义 Pearson III 型分布和广义极值分布分别被选为聚类 1 和 2 的最佳区域分布。结果还表明，可以估计洪水的重现期为 2 年、5 年、25 年甚至更长时间。预计最高的估计洪水发生在 Jelugir-e Majin 站，那里的洪水重现期为 2 年，达到 1034 m³ s⁻¹。在 100 年的重现期内，这增加到 5360 m³ s⁻¹。本研究中介绍的方法推荐用于缺乏完整信息的类似地区。