Precipitation forecast of the Wujiang River Basin based on artificial bee colony algorithm and backpropagation neural network
Yongtao Wang, Jian Liu, Li Rong, Xinyu Suo, Enhui Lu
Abstract
This paper innovatively combines the artificial bee colony (ABC) algorithm and the backpropagation neural network (BPNN) into a precipitation prediction model. The research data were collected by 17 stations in the Wujiang River Basin from 1961 to 2018, and compiled into a time series of precipitation data. Through wavelet analysis on precipitation series, the authors identified the features of precipitation distributions in time and frequency domains at different timescales, and demonstrated the inter-annual trend and abnormalities of precipitation in the basin. Next, the weights and thresholds of the BPNN was optimized by the ABC algorithm, and used to predict the precipitation of the basin in the next two decades. The predicted results were consistent with the periodicity and break points obtained by the wavelet analysis. The Z index was introduced to identify the flood years and drought years in the prediction period. The research results shed new light on climate prediction, flood control and drought resistance.