Identifying homogeneous hydrological zones for flood prediction using multivariable statistical methods and machine learning

Abstract

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.