Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
Underground fires are characterized by smouldering combustion with a slow rate of spread rate and without flames.Although smouldering combustion releases large amounts of gaseous pollutants,it is difficult to discover...Underground fires are characterized by smouldering combustion with a slow rate of spread rate and without flames.Although smouldering combustion releases large amounts of gaseous pollutants,it is difficult to discover by today's forest fire monitoring technologies.Carbon monoxide(CO),nitrogen oxides(NO_(x))and sulfur dioxide(SO_(2))were identified as high concentration marker gases of smouldering combustion-easily-be monitored.According to a two-way ANOVA,combustion time had a significant impact on CO and NO_(x) emissions;smoldering-depth also had a significant impact on NO_(x) emissions but not on CO emissions.Gas emission equations were established by multiple linear regression,C_(co)=156.989-16.626 t and C_(NOx)=3.637-0.252 t-0.039 h.展开更多
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
基金supported financially by the National Key Research and Development Plan(2018YFD0600205)the National Natural Science Foundation of China(31971669)。
文摘Underground fires are characterized by smouldering combustion with a slow rate of spread rate and without flames.Although smouldering combustion releases large amounts of gaseous pollutants,it is difficult to discover by today's forest fire monitoring technologies.Carbon monoxide(CO),nitrogen oxides(NO_(x))and sulfur dioxide(SO_(2))were identified as high concentration marker gases of smouldering combustion-easily-be monitored.According to a two-way ANOVA,combustion time had a significant impact on CO and NO_(x) emissions;smoldering-depth also had a significant impact on NO_(x) emissions but not on CO emissions.Gas emission equations were established by multiple linear regression,C_(co)=156.989-16.626 t and C_(NOx)=3.637-0.252 t-0.039 h.
