Frequency Analysis of Rainfall with Different Methods based on the Missing Data Processed by Random Forest Algorithm

Abstract

Global warming is causing dramatic climate change, leading to rainfall that triggers a more severe risk of flooding. The conventional moment method and linear moment method’s basic theories for the design of flood frequency analyses were introduced and compared for frequency analysis of rainfall. The rainfall data from 149 long-series representative rainfall stations in Jiangsu Province were processed using the random forest (RF) algorithm to address the missing data encountered during the actual rainfall monitoring process. The frequency was calculated using conventional and linear moment methods, and the differences and advantages of the two methods were analyzed by comparing the calculation results of different sites under each method. The results show that under low design frequencies, both the conventional moment and linear moment methods exhibit minimal errors, rendering them suitable for calculating design rainfall. The linear moment method outperforms the conventional moment method in terms of the unbiasedness of the estimation process and for very large values, and that the parameters estimated by the linear moment method are more accurate. In practical hydrological frequency calculations, different computation methods can be chosen according to specific needs to enhance calculation accuracy.