Precipitation State Forecasting Based on Unascertained CMeans and Markov Chain Model with Gray Relevancy Weights

At present, in the field of hydrology and meteorological science, precipitation state forecasting is an extremely important problem. In this paper, the problem of precipitation state forecasting was studied, and a new forecasting method based unascertained c-means and Markov chain model with gray relevancy weights was presented. The method included the unascertained characteristic of precipitation state comprehensively, thus its forecasting outcomes are more scientific. Firstly, the unascertained C-means method is applied to divide time series of precipitation state, and the unascertained classification standard of precipitation state is established based on the fact that there are a lot of unascertained characteristics in the precipitation. Secondly, a forecasting method, called Markov chain model with gray relevancy weights, is applied to predict the future precipitation state by regarding the gray relevancy weights based on the special characteristics of precipitation being a dependent stochastic variable. Finally, the correction and feasibility of this model is identified by a case study.