Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting

Abstract

This study proposes an evolutionary fuzzy inference model that combines a fuzzy inference model, genetic programming (GP), and a genetic algorithm (GA) to forecast flood stages during typhoons. The number of fuzzy inference rules in the proposed approach is based on the number of typhoon flood events. The consequent part of the rule was formed by constructing GP models that depict the rainfall–stage relationship of a specific flood event, whereas the GA was used to search the parameters of the fuzzy membership functions in the premise part of the rule. This study uses the proposed event-based evolutionary fuzzy inference model to forecast the typhoon flood stages of Wu River in Taiwan. Forecasting results based on stage hydrographs and performance indices verify the forecasting ability of the proposed model. This study also identifies the weights of triggered fuzzy rules during the fuzzy inference process, showing that a fuzzy rule is triggered according to the characteristics of the flood event that forms the rule. Moreover, physical explanation of the proposed evolutionary fuzzy inference model was discussed.