基于机器学习算法的森林火灾风险评估研究  

Research on forest fire risk evaluation based on machine learning algorithm

作　　者：李史欣 张福全[1] 林海峰[1] LI Shixin;ZHANG Fuquan;LIN Haifeng(College of Information Science and Technology,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学信息科学技术学院,江苏南京210037

出　　处：《南京林业大学学报（自然科学版）》2023年第5期49-56,共8页Journal of Nanjing Forestry University：Natural Sciences Edition

基　　金：江苏省重点研发计划(BE2021716)。

摘　　要：【目的】利用森林火灾风险图可提高有效巡护,优化有限防火资源,基于地形、人类活动、植被和气象因素数据,采用基于机器学习算法构建了林火发生预测模型,对林火防护提供一定的参考。【方法】以安徽省滁州韭山为研究对象,提取林区的坡度、海拔、坡向、到居住点的距离、到道路的距离、地形湿度指数、归一化植被指数和温度驱动因素,评估火灾发生驱动因子,将潜在驱动因子分成地形、人类活动、植被与气象因素等4类;使用哨兵火灾产品,提取林区内的历史火点,然后采用机器学习算法建立林火发生的预测模型;最后利用混淆矩阵评估指标和接受者操作特征曲线(ROC)进行精度评价。【结果】植被、温度和到道路的距离是研究区域火灾发生的主要驱动因素。两种模型的ROC曲线表明,逻辑回归预测模型准确度为71.07%,曲线下面积(AUC)值为0.7172;随机森林模型具有较好的准确性,准确度达到84.91%,曲线下面积值为0.8501。【结论】随机森林模型表现出比逻辑回归模型更好的预测能力。森林火灾风险图表明,随机森林模型预测下,研究区11.91%(29.36 km^(2))位于高、极高风险级别。森林火灾风险图可有效协助林火防护管理者采取适当的林火预防措施,保护森林资源。【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 0.7172.Meanwhile,the random forest model had a better accuracy,with an accuracy of 84.91%and an area under the curve of 0.8501.【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 km^(2))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.

关 键 词：火灾风险 机器学习 预测模型 森林植被 气象因素 

分 类 号：S771.8[农业科学—森林工程]