A Research On Daily Varying Prediction Model For Forest Fuel Moisture

Forest is the one of the most important natural resources on the earth. It plays a decisive role in the evolution of creatures, the reproduction of human beings and the protection of the environment. Thus, how to protect forest resources and prevent forest fire are the major topics that human beings need to consider. It is significant to establish the mathematical model of average moisture content by applying daily meteorological data and SPSS17.0mathematical software.In this paper, based on the observed data of forestry station of Hailin Shihezi of Heilongjiang Province, the daily varying prediction model for forest fuel moisture by differential equations and the daily varying prediction model for forest fuel moisture by time series are built. The results are as follows:First, the daily varying prediction model for forest fuel moisture by differential equations selects daily average temperature, daily average relative humidity and daily average wind speed as affectoi. The research shows that, under this model, forest fuel moisture for the next day can be predicted by daily average temperature, daily average relative humidity and daily average wind speed. The accuracy of this model reaches64%after inspection.Second, average fuel moisture is self-correlative, so the establishing of the model for forest fuel moisture by time series can produce good effect.Finally, based on the model by time series, concrete affectoi has been added. Thus, a more effective model is obtained, which is the combination of the above two models. The accuracy reaches93%, which is much better.