期刊文献+

基于深度学习的内蒙古大兴安岭林区火灾预测建模研究 被引量:1

Deep Learning-based Forest Fire Prediction Model Research in the Daxing'anling Mountains,Inner Mongolia
下载PDF
导出
摘要 [目的]对内蒙古大兴安岭地区的森林火灾进行预测,为森林防火工作的开展提供重要支持。[方法]以内蒙古大兴安岭林区为研究对象,结合MCD64 A1月度火点产品、地形、气候等数据,构建森林火灾潜在影响因子数据集,分别利用卷积神经网络、随机森林、支持向量机模型对研究区森林火灾的发生概率进行预测与可视化,在此基础上对模型效果进行评价并分析森林火灾空间分布特征。[结果]大兴安岭的主要林火驱动因子按重要性值由高到低排序为海拔、平均气温、总降水量、与水域的距离等;CNN、RF、SVM预测森林火灾发生概率的AUC值分别为0.838、0.794、0.788,CNN的精度最高;CNN能够有效划分出森林火灾易感性极高、极低的区域,有利于划分森林火灾的警示区。[结论]CNN模型比RF、SVM模型更适用于大兴安岭林火发生概率的预测;大兴安岭林火风险的空间分布有明显的区域性,主要发生在东南地区。 [Objective]To predict forest fires in the Daxing'anling Mountains of Inner Mongolia and provide important support for forest fire prevention.[Method]Based on the Daxing'anling Mountains of Inner Mongolia as the research object,combined with MCD64 A1 monthly fire point products,terrain,climate and other data,the forest fire potential impact factor data set was constructed,and the convolutional neural network,random forest and support vector machine models were used respectively to predict and visualize the probability of forest fires in the study area.The developed models were evaluated and the spatial distribution characteristics of forest fires were analyzed.[Results]The main driving factors of forest fire in the Daxing'anling Mountains were altitude,average temperature,total precipitation and the distance from water area in order of importance.The AUC value of CNN,RF and SVM was 0.838,0.794 and 0.788,respectively,and the accuracy of CNN was the highest.CNN can effectively divide areas with high and low forest fire susceptibility,which is conducive to dividing forest fire warning areas.[Conclusion]The CNN model is more suitable for predicting the probability of forest fires in the Daxing'anling Mountains than RF and SVM models.The spatial distribution of forest fire risk in the Greater Khingan Mountains is obviously regional,mainly occurring in the southeast region.
作者 张金钰 彭道黎 张超珺 贺丹妮 杨灿灿 ZHANG Jin-yu;PENG Dao-li;ZHANG Chao-jun;HE Dan-ni;YANG Can-can(Key Laboratory of Forest Resources&Environmental Management,National Forestry and Grassland Administration,Beijing Forestry University,Beijing 100083,China;College of Forestry,Shanxi Agricultural University,Jinzhong 030801,Shanxi,China;School of Geographic Information and Tourism,Chuzhou University,Chuzhou 239000,Anhui,China)
出处 《林业科学研究》 CSCD 北大核心 2024年第1期31-40,共10页 Forest Research
关键词 森林 火灾预测 卷积神经网络 森林火灾敏感性 forest fire warning deep learning sensitivity modeling
  • 相关文献

参考文献10

二级参考文献192

共引文献827

同被引文献4

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部