森林火灾风险预测的研究进展及面临的挑战

doi:10.11896/jsjkx.240400177

摘要/Abstract

摘要： 随着全球气候变化和人类活动的加剧,森林火灾事件频发,造成了严重的生态破坏和社会经济损失。森林火灾风险预测作为森林火灾管理和监测的首要措施,具有重要意义。因此,本研究对现有的森林火灾风险预测方法进行了深入分析,按照数据源的不同,将其分为基于地理环境因素、基于遥感与地理信息系统以及基于遥感影像的模型,并详细总结了每类方法的特点,分析了其研究思路、应用范围以及对数据和算法的具体要求。随后,介绍了在森林火灾风险预测领域中相关研究者提出的一些数据集,并对所提及的预测方法的实验结果进行了对比。最后,分析了这3类模型的主要问题,并对未来的研究方向进行了展望。

关键词: 森林火灾, 火灾预测, 遥感, 人工智能, 机器学习, 深度学习

Abstract: With the intensification of global climate change and human activities,forest fire incidents have become increasingly frequent,leading to severe ecological damage and socioeconomic losses.Forest fire risk prediction,as a primary measure for forest fire management and monitoring,has significant importance.Therefore,this study conducts an in-depth analysis of existing forest fire risk prediction methods.These methods are categorized into three types based on different data sources:models based on geographical environmental factors,models based on remote sensing and geographic information systems(GIS),and models based on remote sensing imagery.The characteristics of each method are thoroughly summarized,and their research approaches,application scopes,and specific requirements for data and algorithms are analyzed.Subsequently,this study introduces several datasets proposed by relevant researchers in the field of forest fire risk prediction and compares the experimental results of the mentioned prediction methods.Finally,the major issues associated with the three types of models are analyzed,and future research directions are proposed.

Key words: Forest fires, Fire prediction, Remote sensing, Artificial intelligence, Machine learning, Deep learning

中图分类号:

TP399

杨继翔, 蒋惠萍, 王森, 马轩. 森林火灾风险预测的研究进展及面临的挑战[J]. 计算机科学, 2025, 52(6A): 240400177-8. https://doi.org/10.11896/jsjkx.240400177

YANG Jixiang, JIANG Huiping, WANG Sen, MA Xuan. Research Progress and Challenges in Forest Fire Risk Prediction[J]. Computer Science, 2025, 52(6A): 240400177-8. https://doi.org/10.11896/jsjkx.240400177

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