Renmin Zhujiang (Jan 2023)
Flood Period Classification Forecast Based on Information Fusion and Recognition
Abstract
To solve the low simulation accuracy of flood forecast models caused by limited historical flood data in river basins,this paper employs the K-means clustering method to cluster typical floods by taking reservoir A as the research object.Meanwhile,it analyzes hydrological influencing factors such as rainfall intensity,rainfall center,and weather system,calculates various parameters of confluence models through a genetic optimization algorithm,and adopts a rough set method to explore the relationship between influencing factors and flood period confluence patterns.Finally,the flood period classification forecast based on information fusion and recognition is conducted.The results are as follows:① The absolute and relative errors of the four selected typical floods calculated by the classification forecast method are 9.01 m3/s and 2.95%,116.46 m3/s and 6.78%,30.92 m3/s and 17.55%,and 6.12 m3/s and 1.86% respectively;② The simulation accuracy of the flood classification forecast model built in this paper is higher than that of the traditional forecast methods,and the determination coefficients of different typical floods are all above 0.8.The results can provide references for the flood period classification and forecast in north China and other regions with less flood data.
