基于森林火灾统计资料,采用加拿大森林火险天气指标系统(Canadian Forest Fire Weather Index System,CFFWIS)分析了浙江省森林火险期内森林火险天气指标动态变化趋势。结果表明:研究区火险期内森林火灾发生次数呈递减趋势,但每场火灾...基于森林火灾统计资料,采用加拿大森林火险天气指标系统(Canadian Forest Fire Weather Index System,CFFWIS)分析了浙江省森林火险期内森林火险天气指标动态变化趋势。结果表明:研究区火险期内森林火灾发生次数呈递减趋势,但每场火灾的平均过火面积呈显著增加趋势(P<0.01);细小可燃物湿度码(CFFM)和初始蔓延指标(IIS)达到显著水平(P<0.05),可作为浙江省森林火险期火险状况的良好指标;1991-2011年,研究区森林防火期内可燃物湿度指标、火行为指标及火灾控制难易度指标总体呈增加趋势。其中,春季火险期可燃物干燥状况增加趋势较秋、冬季火险期幅度大,且已达到显著水平(P<0.05)。因此,加大该区的森林火灾防控工作,尤其是春季火险期的防控工作刻不容缓。展开更多
With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation...With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.展开更多
文摘基于森林火灾统计资料,采用加拿大森林火险天气指标系统(Canadian Forest Fire Weather Index System,CFFWIS)分析了浙江省森林火险期内森林火险天气指标动态变化趋势。结果表明:研究区火险期内森林火灾发生次数呈递减趋势,但每场火灾的平均过火面积呈显著增加趋势(P<0.01);细小可燃物湿度码(CFFM)和初始蔓延指标(IIS)达到显著水平(P<0.05),可作为浙江省森林火险期火险状况的良好指标;1991-2011年,研究区森林防火期内可燃物湿度指标、火行为指标及火灾控制难易度指标总体呈增加趋势。其中,春季火险期可燃物干燥状况增加趋势较秋、冬季火险期幅度大,且已达到显著水平(P<0.05)。因此,加大该区的森林火灾防控工作,尤其是春季火险期的防控工作刻不容缓。
基金supported by Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20200710).
文摘With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.
