森林与草原火蔓延预测方法对比研究与展望OA

Fire spread prediction is a critical technical component in wildfire risk assessment and emergency decision-making for both forest and grassland ecosystems.From a cross-ecosystem comparative perspective,this study focuses on forests and grasslands as two representative fuel ecosystems and conducts a systematic comparison and integrated analysis of fire spread prediction models,with particular attention to the pronounced differences among ecosystems in fuel structure,wind sensitivity,and model adaptability.Based on the core principles of fire spread prediction models,existing approaches are systematically reviewed,outlining the technological evolution from physical and empirical models,statistical learning and machine learning models,and deep learning models,to multi-source intelligent fusion models.By considering key factors such as fuel characteristics,wind-field driving features,and computational requirements,the applicability of different models in forest and grassland scenarios is analyzed,and their performance differences in terms of temporal response characteristics and computational efficiency are compared.Within a forest-grassland comparative framework,cross-ecosystem transfer and mutual learning mechanisms between fire spread prediction models are further summarized from the perspectives of mechanism representation,data organization,and algorithm implementation.In addition,emerging trends in current research,including cross-scale coupling,interpretable modeling,multimodal data fusion,and data frame-work construction,are synthesized,providing a systematic reference for model selection,comparison,and methodological research on fire spread prediction under different ecosystem conditions.