Research on Multi-Factor Forest Fire Prediction Model Using Machine Learning Method in China

Abstract

Forest fires can cause serious harm in many ways. Studying the scientific prediction of forest fires is an important basis for preventing such fires. At present, there is little research on the prediction of long time series forest fires in China. Choosing a suitable forest fire prediction model is of great importance to China’s forest fire prevention and control work. Based on data on fire hotspots, meteorology, terrain, vegetation, infrastructure, and socio-economics collected from 2003 to 2016, we used a random forest model as a feature-selection method to determine 13 major drivers of forest fires in China (such as temperature, terrain etc.). The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area-under-the-curve value. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and create a spatial distribution map of the areas with high incidences of forest fires. The results show that the prediction accuracy of the four forest fire prediction models is between 75.8% and 89.2%, and the area-under-the-curve value is between 0.840 and 0.960. The random forest model has the highest accuracy (89.2%) and area-under-the-curve value (0.96). It is used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in northeastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region), southeastern China (including Fujian Province and Jiangxi Province) etc. In those areas at high risk of forest fires, the management departments can improve the forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study not only helps in understanding the main drivers of forest fires in China, but it also provides a reference for the selection of high-precision forest fire prediction models and provides a scientific basis for China’s forest fire prevention and control work.

A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding

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