Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China

Abstract

Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms. The Heihe area of Heilongjiang Province is one of the key forest areas and forest-fire-prone areas in China. Based on daily historical forest-fire data from 1995 to 2015, daily meteorological data, topographic data and basic geographic information data, the main forest-fire driving factors were first analyzed by using RF importance characteristic evaluation and logistic stepwise regression. Then, the prediction models were established by using the two machine learning methods. Furthermore, the goodness of fit of the models was tested using the receiver operating characteristic test method. Finally, the fire-risk grades were divided by applying the kriging method. The results showed that 11 driving factors were significantly correlated with forest-fire occurrence, and days after the last rain, daily average relative humidity, daily maximum temperature, daily average water vapor pressure, daily minimum relative humidity and distance to settlement had a high correlation with the risk of forest-fire occurrence. The prediction accuracy of the two algorithms in regard to fire points was higher than that for nonfire points. The overall prediction accuracy and goodness of fit of the RF and BPNN algorithms were similar. The two methods were both suitable for forest-fire occurrence prediction. The high-fire-risk zones were mainly concentrated in the northwestern and central parts of the Heihe area.