Improved Prediction of Forest Fire Risk in Central and Northern China by a Time-Decaying Precipitation Model

Abstract

With the increase in extreme climate events, forest fires burn in much larger areas. Therefore, it is important to accurately predict forest fire frequencies. Precipitation is an important factor that affects the probability of future forest fires. Previous models used average precipitation values, but the attenuation of precipitation was not considered. In this study, a time-decaying precipitation algorithm was used to calculate the comprehensive precipitation index. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, observed fire spots were converted into a continuous density of fire spots. The structure of the prediction model is more realistic, which is conducive to obtaining higher-precision prediction results. Additionally, the support vector machine (SVM) regression model was used to construct a forest fire warning model. When the comprehensive precipitation index was compared with the average precipitation value, the accuracy of the four forest areas in central and northern China in the test set was improved by approximately 10%. The findings are relevant to forest ecologists and managers for future mitigation of forest fires, and also for successful prediction of other fire-prone areas.