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
【背景】自1995年至今,小麦赤霉病(Fusarium head blight,FHB)逐渐在海河平原蔓延,由零星出现演变成连片发生,在流行年份呈现出暴发快、面积大、损失重的特点,小麦赤霉病已由次要病害上升为主要病害之一。准确的预测预报是有效控制小麦...【背景】自1995年至今,小麦赤霉病(Fusarium head blight,FHB)逐渐在海河平原蔓延,由零星出现演变成连片发生,在流行年份呈现出暴发快、面积大、损失重的特点,小麦赤霉病已由次要病害上升为主要病害之一。准确的预测预报是有效控制小麦赤霉病发生与发展的关键和难点。【目的】根据海河平原小麦赤霉病发生情况的监测分析,构建适宜的小麦赤霉病预测模型,为科学防控赤霉病提供技术支撑。【方法】基于2001-2016年海河平原21个小麦主产县(市)的赤霉病病穗率数据,以及小麦关键生育期内的气象数据,采用逐步回归分析,筛选影响小麦赤霉病发生的关键气象因子,构建基于多元线性回归模型和增强回归树模型的小麦赤霉病发生预测模型。【结果】明确了增强回归树模型的学习效率(lr)为0.005、树的复杂度(tc)为6时,模型的预测偏差最低,残差标准误为0.006311;筛选出8个对海河平原小麦赤霉病发生影响显著的关键气象因子,即MRH15、Rain-35、MRH-55、SD15、LT-65、MWS-55、MT-25、DRain15,并构建了含有8个预测变量的多元线性回归模型(R^(2)=0.8158,矫正R^(2)=0.8018,P<2.2×10^(-16))。同时,应用增强回归树模型评估了上述8个关键气象因子的重要性,分别为69.62%、14.08%、4.89%、4.34%、3.35%、2.02%、1.20%、0.50%;根据重要的预测变量进一步简化预测模型,构建了含有4个预测变量的多元线性回归模型(y=-19.45376+0.11689MRH15+0.17346Rain-35+0.04185SD15+0.26592MRH-55,R^(2)=0.7575,矫正R^(2)=0.7468,P<2.2×10^(-16));当预测变量由8个调减至4个时,利用2008、2010、2012年安新、定州、馆陶等地历史数据验证模型预测病穗率的准确度,多元线性回归模型预测准确度由88.43%降至85.90%,增强回归树模型预测准确度由87.72%升至91.23%;利用2001-2016年正定、栾城的历史数据验证模型预测病穗率的准确度,两个模型预测准确度无显著变化,多元线性回归模型预测准确度由87.53%变为87.42%,增强回归树模型预测准确度由89.20%变为89.21%。整体而言,多元线性回归模型预测准确度呈下降趋势,而增强回归树模型预测准确度呈上升趋势。【结论】研究构建了含有4个预测变量的增强回归树模型,其预测准确度达89.21%,病穗率预测值与实际观测值的波动趋势基本一致,表明增强回归树模型在海河平原小麦赤霉病预测预报中具有很好的应用前景。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.
文摘【背景】自1995年至今,小麦赤霉病(Fusarium head blight,FHB)逐渐在海河平原蔓延,由零星出现演变成连片发生,在流行年份呈现出暴发快、面积大、损失重的特点,小麦赤霉病已由次要病害上升为主要病害之一。准确的预测预报是有效控制小麦赤霉病发生与发展的关键和难点。【目的】根据海河平原小麦赤霉病发生情况的监测分析,构建适宜的小麦赤霉病预测模型,为科学防控赤霉病提供技术支撑。【方法】基于2001-2016年海河平原21个小麦主产县(市)的赤霉病病穗率数据,以及小麦关键生育期内的气象数据,采用逐步回归分析,筛选影响小麦赤霉病发生的关键气象因子,构建基于多元线性回归模型和增强回归树模型的小麦赤霉病发生预测模型。【结果】明确了增强回归树模型的学习效率(lr)为0.005、树的复杂度(tc)为6时,模型的预测偏差最低,残差标准误为0.006311;筛选出8个对海河平原小麦赤霉病发生影响显著的关键气象因子,即MRH15、Rain-35、MRH-55、SD15、LT-65、MWS-55、MT-25、DRain15,并构建了含有8个预测变量的多元线性回归模型(R^(2)=0.8158,矫正R^(2)=0.8018,P<2.2×10^(-16))。同时,应用增强回归树模型评估了上述8个关键气象因子的重要性,分别为69.62%、14.08%、4.89%、4.34%、3.35%、2.02%、1.20%、0.50%;根据重要的预测变量进一步简化预测模型,构建了含有4个预测变量的多元线性回归模型(y=-19.45376+0.11689MRH15+0.17346Rain-35+0.04185SD15+0.26592MRH-55,R^(2)=0.7575,矫正R^(2)=0.7468,P<2.2×10^(-16));当预测变量由8个调减至4个时,利用2008、2010、2012年安新、定州、馆陶等地历史数据验证模型预测病穗率的准确度,多元线性回归模型预测准确度由88.43%降至85.90%,增强回归树模型预测准确度由87.72%升至91.23%;利用2001-2016年正定、栾城的历史数据验证模型预测病穗率的准确度,两个模型预测准确度无显著变化,多元线性回归模型预测准确度由87.53%变为87.42%,增强回归树模型预测准确度由89.20%变为89.21%。整体而言,多元线性回归模型预测准确度呈下降趋势,而增强回归树模型预测准确度呈上升趋势。【结论】研究构建了含有4个预测变量的增强回归树模型,其预测准确度达89.21%,病穗率预测值与实际观测值的波动趋势基本一致,表明增强回归树模型在海河平原小麦赤霉病预测预报中具有很好的应用前景。
