Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,...Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.展开更多
发生火灾时因过火时间、最高温度和受火形式不同,导致混凝土构件表面酥松开裂深度、变形损伤程度差异较大。论文以六层混凝土框架主控楼电缆层发生火灾为例,通过对火灾后现场勘察确定过火时间;对火灾现场混凝土取样,采用 X 射线衍射、...发生火灾时因过火时间、最高温度和受火形式不同,导致混凝土构件表面酥松开裂深度、变形损伤程度差异较大。论文以六层混凝土框架主控楼电缆层发生火灾为例,通过对火灾后现场勘察确定过火时间;对火灾现场混凝土取样,采用 X 射线衍射、热重分析及扫描电镜推定火场不同区域温度;采用 ABAQUS 有限元软件进行了火灾温度场下结构性能数值分析。在非均匀火灾温度场中,温度附加应力和材料性能劣化相耦合是导致混凝土结构丧失承载能力、发生突然垮塌的主要原因。展开更多
基金financially supported by the National Natural Science Fundation of China(Grant Nos.42161065 and 41461038)。
文摘Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.
文摘发生火灾时因过火时间、最高温度和受火形式不同,导致混凝土构件表面酥松开裂深度、变形损伤程度差异较大。论文以六层混凝土框架主控楼电缆层发生火灾为例,通过对火灾后现场勘察确定过火时间;对火灾现场混凝土取样,采用 X 射线衍射、热重分析及扫描电镜推定火场不同区域温度;采用 ABAQUS 有限元软件进行了火灾温度场下结构性能数值分析。在非均匀火灾温度场中,温度附加应力和材料性能劣化相耦合是导致混凝土结构丧失承载能力、发生突然垮塌的主要原因。
