Prediction model of annual precipitation based on information-diffusion approximate reasoning
Qiang Huang, Yuan WenLin, Xiaonan Chen, Hongbo Zhang, Yimin Wang · Journal of Northwest A&F University · 2009
【Objective】The method of applying information-diffusion approximate reasoning to predict annual precipitation was studied and compared with other methods in order to analyze the application prospects.【Method】According to features of precipitation time-series,prediction rules were suggested based on current tendency and the neighbor year precipitation.This may help the information-diffusion approximate reasoning describe the complex nonlinear relation in precipitation data.Applying the rules a precipitation time-series in an irrigation area as an example,the forecasting result was obtained.【Result】The information-diffusion approximate reasoning method had fewer errors and a better effect on precipitation prediction than artificial neural network and linear autoregressive method.【Conclusion】The method can transfer sample points to fuzzy sets and take advantage of more information,and even may switch conflict mode to compatible mode.Results indicate that annual precipitation prediction with information-diffusion approximate reasoning model is good at the mining of uncertain knowledge,and can find out more information and make the data series more smoothly than traditional methods.In fact,this is a new data mining model for time-series.Any similar problem can be dissolved better by the method.It is significant to spread this method.