Abstract:

【Objective】 Forest fire risk maps are necessary to improve effective patrols and optimize the scientific layout of limited fire prevention resources. This study uses machine learning algorithms to construct forest fire occurrences based on terrain, human activities, vegetation, and meteorological factor data.【Method】With Jiushan Mountain in Chuzhou city in Anhui province as the research object, we extracted the following potential driving factors: slope, elevation, aspect, distance to settlement, distance to road, topographic wetness index, normalized difference vegetation index and temperature of the study area, evaluated the driving factors of fire occurrence, and then divided the potential driving factors into topography, human activities, vegetation, meteorological factors, and other four categories. Historical fire points in the forest area were extracted from the sentinel fire products. A prediction model for forest fire occurrence was then constructed using a machine learning algorithm. Finally, the accuracy of the models was evaluated using a confusion matrix and receiver operating characteristic curves. 【Result】We found that vegetation, temperature and distance to the road are the main driving factors of forest fires in the study area. The ROC curves of the two models showed that the Logistic regression prediction model had an accuracy of 71.07%, where the area under the curve was 0.717 2. Meanwhile, the random forest model had a better accuracy, with an accuracy of 84.91% and an area under the curve of 0.850 1.【Conclusion】The random forest model exhibits a better predictive ability than that does the logistic regression model. Furthermore, the generated forest fire risk map shows that 11.91% (29.36 km²) of the study area is at a high or extremely a high risk. If utilized, this forest fire risk map can effectively help forest fire protection managers implement appropriate measures to protect forest resources in Jiushan Mountain.

Key words: fire risk, machine learning, forecasting model, forest vegetation, meteorological factors