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
Forest fires are influenced by several factors,including forest location,species type,age and density,date of fire occurrence,temperatures,and wind speeds,among others.This study investigates the quantitative effects ...Forest fires are influenced by several factors,including forest location,species type,age and density,date of fire occurrence,temperatures,and wind speeds,among others.This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management.Data on forest fire damage from 1969 to 2013 was analyzed.The results indicate that different forest locations and types,fire occurrence dates,temperatures,and wind speeds were statistically significant.The eastern regions of the study area experienced the highest fire occurrence,accounting for 85.0%of the total number of fires as well as the largest average forested area burned.April,May,and October had more frequent fires than other months,accounting for 78.9%,while September had the most extensive forested area burned(63.08 ha)and burnt area(106.34 ha).Hardwood mixed forest and oak forest had more frequent fires,accounting for 31.9%and 26.0%,respectively.Hardwood-conifer mixed forest had the most forested area burned(50.18 ha)and burnt area(65.09 ha).Temperatures,wind speeds,and their interaction had significant impacts on forested area burned and area burnt.展开更多
Underground fires are a smoldering combustion with a slow spread rate, low temperatures and no flame. They can last from days to several months, and can even become overwintering fires. They are difficult to find, lea...Underground fires are a smoldering combustion with a slow spread rate, low temperatures and no flame. They can last from days to several months, and can even become overwintering fires. They are difficult to find, leading to considerable damage to the forests. The moisture content of combustible fuels is an important factor in the occurrence and persistence of underground forest fires. The Daxing’an Mountains are a hot spot for underground fires in China. This paper looks at the influence of different moisture contents on underground fire characteristics using simulation combustion experiments in the laboratory. The study showed that peak temperature and spread rate fluctuation of humus at different moisture levels increased with humus depth. Peak temperature and spread rate fluctuation of humus at different depths decreased with increased moisture;moisture content and depth of humus had a significant effect on peak temperature and spread rate fluctuation;peak temperature at different depths decreased with increased moisture;the spread rate in upper layers increased with moisture content, while the spread rate in the lower layers decreased with increased moisture content.展开更多
Underground fires are slow spreading,long-lasting and low temperature smoldering combustion without flames,mainly occurring in peatlands and wetlands with rich organic matter.The spread of the smoldering is maintained...Underground fires are slow spreading,long-lasting and low temperature smoldering combustion without flames,mainly occurring in peatlands and wetlands with rich organic matter.The spread of the smoldering is maintained by heat released during combustion and monitoring this is an important approach to detect underground fires.The Daxing'an Mountains region is a hotspot for underground fires in northeast China.This study examined a L arix gmelinii plantation in the Tatou wetlands of the Daxing'an Mountains and determined the maximum temperature variation of humus of varying particle sizes,and the temperature rising process based on non-linear mixed effects models by an indoor combustion experiment.Maximum combustion temperatures up to 897.5°C,increased with humus depth;among the three models tested,Richard's equations were best for characterizing temperature variations;a non-linear equation with three parameters had the highest accuracy in fitting the combustion temperature variations with varying humus particle sizes.These results are informative for predicting temperature variations and provide technical support for underground fire monitoring.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.
基金supported financially by the National Key Research and Development Plan(2018YFD0600205)China’s National Foundation of Natural Sciences(31470497)the Project of Jilin Province Department of Education(JJKH20180347KJ)
文摘Forest fires are influenced by several factors,including forest location,species type,age and density,date of fire occurrence,temperatures,and wind speeds,among others.This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management.Data on forest fire damage from 1969 to 2013 was analyzed.The results indicate that different forest locations and types,fire occurrence dates,temperatures,and wind speeds were statistically significant.The eastern regions of the study area experienced the highest fire occurrence,accounting for 85.0%of the total number of fires as well as the largest average forested area burned.April,May,and October had more frequent fires than other months,accounting for 78.9%,while September had the most extensive forested area burned(63.08 ha)and burnt area(106.34 ha).Hardwood mixed forest and oak forest had more frequent fires,accounting for 31.9%and 26.0%,respectively.Hardwood-conifer mixed forest had the most forested area burned(50.18 ha)and burnt area(65.09 ha).Temperatures,wind speeds,and their interaction had significant impacts on forested area burned and area burnt.
基金financially supported by the National Natural Science Foundation of China (31971669)the Postgraduate Innovation Project of Beihua University (2021-013)
文摘Underground fires are a smoldering combustion with a slow spread rate, low temperatures and no flame. They can last from days to several months, and can even become overwintering fires. They are difficult to find, leading to considerable damage to the forests. The moisture content of combustible fuels is an important factor in the occurrence and persistence of underground forest fires. The Daxing’an Mountains are a hot spot for underground fires in China. This paper looks at the influence of different moisture contents on underground fire characteristics using simulation combustion experiments in the laboratory. The study showed that peak temperature and spread rate fluctuation of humus at different moisture levels increased with humus depth. Peak temperature and spread rate fluctuation of humus at different depths decreased with increased moisture;moisture content and depth of humus had a significant effect on peak temperature and spread rate fluctuation;peak temperature at different depths decreased with increased moisture;the spread rate in upper layers increased with moisture content, while the spread rate in the lower layers decreased with increased moisture content.
基金funded by the National Natural Science Foundation of China(Grant No.31971669)。
文摘Underground fires are slow spreading,long-lasting and low temperature smoldering combustion without flames,mainly occurring in peatlands and wetlands with rich organic matter.The spread of the smoldering is maintained by heat released during combustion and monitoring this is an important approach to detect underground fires.The Daxing'an Mountains region is a hotspot for underground fires in northeast China.This study examined a L arix gmelinii plantation in the Tatou wetlands of the Daxing'an Mountains and determined the maximum temperature variation of humus of varying particle sizes,and the temperature rising process based on non-linear mixed effects models by an indoor combustion experiment.Maximum combustion temperatures up to 897.5°C,increased with humus depth;among the three models tested,Richard's equations were best for characterizing temperature variations;a non-linear equation with three parameters had the highest accuracy in fitting the combustion temperature variations with varying humus particle sizes.These results are informative for predicting temperature variations and provide technical support for underground fire monitoring.
