The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often d...The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.展开更多
以大兴安岭地区南瓮河保护区落叶松林(Larix gmelinii)、蒙古栎林(Quercus mongolica Fischer)、落叶松-白桦混交林(Mixture of Larix gmelinii and Betula platyphylla)(阴坡、阳坡)、沟塘草甸等4种典型林分为研究对象,运用气象要素回...以大兴安岭地区南瓮河保护区落叶松林(Larix gmelinii)、蒙古栎林(Quercus mongolica Fischer)、落叶松-白桦混交林(Mixture of Larix gmelinii and Betula platyphylla)(阴坡、阳坡)、沟塘草甸等4种典型林分为研究对象,运用气象要素回归法,对春季防火期和秋季防火期内的地表细小死可燃物含水率动态进行测定,构建了不同防火期、不同林型地表死可燃物含水率的预测模型,分析了相应模型的预测误差。结果表明:同林型地表可燃物含水率在春季防火期和秋季防火期差异显著;在秋季防火期,5个典型林型的地表死可燃物含水率预测平均绝对误差为0.167,平均相对误差为0.218,低于春季防火期模型和春季-秋季混合模型;秋季防火期模型对可燃物含水率预测效果最好。气象要素回归法适用于南瓮河保护区典型林型地表死可燃物含水率预测。展开更多
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.2572020AW43NO.2572019CP19)+2 种基金the National Natural Science Foundation of China(Grant No.31470715)the Natural Science Foundation of Hei-longjiang Province(Grant No.TD2020C001)the project for cultivating excellent doctoral dissertation of forestry engineering(Grant No.LYGCYB202009).
文摘The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.
文摘以大兴安岭地区南瓮河保护区落叶松林(Larix gmelinii)、蒙古栎林(Quercus mongolica Fischer)、落叶松-白桦混交林(Mixture of Larix gmelinii and Betula platyphylla)(阴坡、阳坡)、沟塘草甸等4种典型林分为研究对象,运用气象要素回归法,对春季防火期和秋季防火期内的地表细小死可燃物含水率动态进行测定,构建了不同防火期、不同林型地表死可燃物含水率的预测模型,分析了相应模型的预测误差。结果表明:同林型地表可燃物含水率在春季防火期和秋季防火期差异显著;在秋季防火期,5个典型林型的地表死可燃物含水率预测平均绝对误差为0.167,平均相对误差为0.218,低于春季防火期模型和春季-秋季混合模型;秋季防火期模型对可燃物含水率预测效果最好。气象要素回归法适用于南瓮河保护区典型林型地表死可燃物含水率预测。
