杜帅, 单延龙, 尹赛男, 等.2016. 加拿大林火天气指标系统在通化市的应用. 北华大学学报:自然科学版,17(2):176-180. (Du S, Shan Y L, Yin S N, et al. 2016. Applying Canadian forest fire weather index system in Tonghua City. Journal of Beihua University:Natural Science, 17(2):176-180.[in Chinese]) 田晓瑞, 舒立福, 赵凤君, 等. 2010. 大兴安岭地区森林火险变化及 FWI 适用性评估. 林业科学,46(5):127-132. (Tian X R, Shu L F, Zhao F J, et al. 2010. Changes of forest fire danger and the evaluation of the FWI System application in the Daxing'anling region. Scientia Silvae Sinicae,46(5):127-132.[in Chinese]) 王周, 金万洲, 2015. 基于地理加权泊松模型的河南省火灾风险模拟. 南京林业大学学报:自然科学版,39(5):93-98. (Wang Z, Jin W Z. 2015. Fire danger modeling with Geographically Weighted Poisson Model in Henan Province. Journal of Nanjing Forestry University:Natural Sciences Edition, 39(5):93-98.[in Chinese]) 于淼. 2016. 北京房山林火发生预测模型及小班火险等级区划研究.北京:北京林业大学硕士学位论文. (Yu M.2016. The research of forest fire prediction model in Fangshan District, Beijing and sublot fire danger Rating division. Beijing:MS thesis of Beijing Forestry University.[in Chinese]) 张海军, 2014. 河南省火灾影响因素的空间分析. 地理科学进展,33(7):958-968. (Zhang H J. 2014. Spatial analysis of fire-influencing factors in Henan Province. Progress in Geography, 33(7):958-968.[in Chinese]) 张雷, 王琳琳, 张旭东, 等. 2014. 随机森林算法基本思想及其在生态学中的应用——以云南松分布模拟为例. 生态学报,34(3):650-659. (Zhang L, Wang L L, Zhang X D, et al.2014. The basic principle of random forest and its applications in ecology:a case study of Pinus yunnanensis. Acta Ecologica Sinica, 34(3):650-659.[in Chinese]) 曾钦文, 曾思亮, 王辉, 等.2017. 龙川县森林火险等级预报方法的建立及应用. 广东气象,39(4):52-55. (Zeng Q W,Zeng S L,Wang H, et al. 2017. Establishment and application of forest fire grade forecasting method in Longchuan County. Guangdong Meteorology,39(4):52-55.[in Chinese]) 宋超. 2017. 面向城市消防站选址规划的时空动态火灾风险建模分析.合肥:中国科学技术大学博士学位论文. (Song C. 2017. The spatiotemporal dynamic modeling analysis of fire risk for the location planning of urban fire stations. Hefei:PhD thesis of University of Science and Technology of China.[in Chinese]) Breiman L. 2001. Random forests. Machine Learning, 45(1):5-32. Fox D, Martin N, Carrega P, et al. 2015. Increases in fire risk due to warmer summer temperatures and wildland urban interface changes do not necessarily lead to more fires. Applied Geography,56:1-12. Guo F, Su Z, Tigabu M, et al. 2017. Spatial modelling of fire drivers in urban-forest ecosystems in China. Forests,8:180. Naghibi S A, Pourghasemi H R, Dixon B. 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment,188(1):1-27. Oliveira S, Oehler F, San-Miguel-Ayanz J,et al. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management,275:117-129. Radeloff V C, Hammer R B, Stewart S I, et al. 2005. The wildland-urban interface in the United States. Ecological Applications, 15(3):799-805. Reid C E, Jerrett M, Petersen M L, et al. 2015. Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning. Environmental Science & Technology, 49(6):3887-3896. Rodrigues M, de la Riva J, Fotheringham S. 2014. Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Applied Geography, 48:52-63. Song C, Kwan M P, Zhu J. 2017. Modeling fire occurrence at the city scale:A comparison between geographically weighted regression and global linear regression. International Journal of Environmental Research and Public Health, 14:396. Wu Z, He H S, Yang J, et al. 2014. Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. Science of the Total Environment,493:472-80. |