Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example

Bai Haifeng; Liu Xiaodong; Niu Shukui; He Yadong

doi:10.12171/j.1000-1522.20200173

Journal of Beijing Forestry University > 2021 > 43(5): 44-52. > DOI: 10.12171/j.1000-1522.20200173

Bai Haifeng, Liu Xiaodong, Niu Shukui, He Yadong. Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example[J]. Journal of Beijing Forestry University, 2021, 43(5): 44-52. DOI: 10.12171/j.1000-1522.20200173

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Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example

1.
School of Ecology and Nature Reserves, Beijing Forestry University, Beijing 100083, China
2.
College of Mathematics and Statistics, Yunnan University, Kunming 650504, Yunnan, China

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Received Date: June 18, 2020
Revised Date: January 06, 2021
Available Online: April 04, 2021
Published Date: May 26, 2021

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Abstract

Abstract

Objective Based on the Bayesian model averaging method and binomial Logistic regression model, this paper constructs a forest fire prediction model in Dali Prefecture, Yunnan Province of southwestern China, so as to improve the prediction accuracy of forest fire and provide technical support for forest fire management in the study area.
Method Using the forest fire data and corresponding meteorological data of Dali Prefecture from 2000 to 2013, the binomial Logistic regression model and the Bayesian model averaging method were used to empirically analyze the response of forest fires to meteorological factors in this area. The binomial Logistic regression model is a single model. Before modeling, the explanatory variables with significant collinearity were eliminated by multicollinearity test. Then, the final variables were screened by stepwise regression method and the parameters were fitted. The Bayesian average model is a combined model. When modeling based on the Bayesian model averaging method, the Occam’s window method was used to appropriately adjust the model space, and the posterior probabilities of the five optimal models were used as weights for weighted modeling. In this paper, the all sample data were randomly divided into 80% training samples and 20% test samples. A model was built based on the training samples to predict the test samples. The accuracy of the model was calculated by comparing the observations and predictions.
Result Fitting through the binomial Logistic model, the results showed that: the model fitting goodness was 0.783, and the prediction accuracy was 0.718; through the Bayesian average model fitting, the results showed that: the model fitting goodness was 0.868, and the prediction accuracy was 0.807. The comparison of the prediction results of the two models showed that: in the training set, the prediction accuracy of the Bayesian average model was 9.3% higher than that of the binomial Logistic regression model; and in the test set, the former was 8.9% higher than the latter.
Conclusion In the prediction model of forest fire occurrence in Dali Prefecture based on meteorological factors, the goodness of fit and prediction accuracy of Bayesian average model were higher than that of binomial Logistic model, indicating that the Bayesian model averaging method had certain practical application significance. It can be used to improve the prediction accuracy of forest fire in the study area, which is beneficial to the decision management of forest fire.
- Dali Prefecture,
- forest fire,
- meteorological factor,
- Bayesian model averaging method,
- Logistic regression

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References

[1]	Rigo D D, Giorgio L, Durrant T H, et al. Forest fire danger extremes in Europe under climate change: variability and uncertainty[M]. Luxembourg: Publications Office of the European Union, 2017.
[2]	田晓瑞, 宗学政, 舒立福, 等. ENSO事件对中国森林火险天气的影响[J]. 应用生态学报, 2020, 31(5):65−73. Tian X R, Zong X Z, Shu L F, et al. Impacts of ENSO events on forest fire weather of China[J]. Chinese Journal of Applied Ecology, 2020, 31(5): 65−73.
[3]	白夜, 武英达, 贾宜松, 等. 2019—2020年澳大利亚气候异常与山火爆发的关系分析及应对策略[J]. 中国应急救援, 2020(2):23−27. doi: 10.3969/j.issn.1673-5579.2020.02.006 Bai Y, Wu Y D, Jia Y S, et al. Link between climate anomaly and Australia bushfires in 2019−2020[J]. China Emergency Rescue, 2020(2): 23−27. doi: 10.3969/j.issn.1673-5579.2020.02.006
[4]	赵凤君, 舒立福. 森林草原火灾扑救安全学[M]. 北京: 中国林业出版社, 2015. Zhao F J, Shu L F. Forest and grassland fire fighting safety[M]. Beijing: China Forestry Publishing House, 2015.
[5]	岳超, 罗彩访, 舒立福, 等. 全球变化背景下野火研究进展[J]. 生态学报, 2020, 40(2):385−401. Yue C, Luo C F, Shu L F, et al. A review on wildfire studies in the context of global change[J]. Acta Ecologica Sinica, 2020, 40(2): 385−401.
[6]	Marlon J R, Bartlein P J, Gavin D G, et al. Long-term perspective on wildfires in the western USA[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(9): 3203−3204.
[7]	Westerling A L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring[J]. Philosophical Transactions of the Royal Society of London (Series B): Biological Sciences, 2016, 371: 1−10.
[8]	潘登, 郁培义, 吴强. 基于气象因子的随机森林算法在湘中丘陵区林火预测中的应用[J]. 西北林学院学报, 2018, 33(3):175−183. Pan D, Yu P Y, Wu Q. Application of random forest algorithm on the forest fire prediction based on meteorological factors in the hilly area, central Hunan Province[J]. Journal of Northwest Forestry University, 2018, 33(3): 175−183.
[9]	North M P, Stephens S L, Collins B M, et al. Reform forest fire management[J]. Science, 2015, 349: 1280−1281. doi: 10.1126/science.aab2356
[10]	Fischer A P, Spies T A, Steelman T A, et al. Wildfire risk as a socioecological pathology[J]. Frontiers in Ecology and the Environment, 2016, 14(5): 276−284. doi: 10.1002/fee.1283
[11]	Zhang G, Wang M, Liu K. Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China[J]. International Journal of Disaster Risk Science, 2019, 10(3): 386−403. doi: 10.1007/s13753-019-00233-1
[12]	Murphy T E, Tsang S W, Leo L S, et al. Bayesian model averaging for selection of a risk prediction model for death within thirty days of discharge: the silver-ami study[J]. International Journal of Statistics in Medical Research, 2019, 8: 1−7. doi: 10.6000/1929-6029.2019.08.01
[13]	Huang H, Liang Z, Li B, et al. Combination of multiple data-driven models for long-term monthly runoff predictions based on Bayesian model averaging[J]. Water Resources Management, 2019, 33(9): 3321−3338. doi: 10.1007/s11269-019-02305-9
[14]	王倩, 师鹏飞, 宋培兵, 等. 基于贝叶斯模型平均法的洪水集合概率预报[J]. 水电能源科学, 2016(6):64−66. Wang Q, Shi P F, Song P B, et al. Multi-model ensemble flood probability forecasting based on BMA[J]. Water Resources and Power, 2016(6): 64−66.
[15]	张畅, 陈新军. 海洋环境因子对澳洲鲐亲体补充量关系的影响: 基于贝叶斯模型平均法的研究[J]. 海洋学报, 2019, 41(2):99−106. Zhang C, Chen X J. The effect of environmental factors on stock-recruitment relationship of spotted mackerel-based on Bayesian model averaging method[J]. Haiyang Xuebao, 2019, 41(2): 99−106.
[16]	李丽琴. 云南省森林火灾发生与气象因子之间的关系研究[D]. 北京: 北京林业大学, 2010. Li L Q. Study on the relationship between forest fires and the meteorological factors in Yunnan[D]. Beijing: Beijing Forestry University, 2010.
[17]	周明昆, 王永平, 高月忠. 气象因子对云南大理森林火灾的影响[J]. 四川林业科技, 2012, 33(6):96−99. doi: 10.3969/j.issn.1003-5508.2012.06.022 Zhou M K, Wang Y P, Gao Y Z. Effects of meteorological factors on forest fires in Dali, Yunnan[J]. Journal of Sichuan Forestry Science and Technology, 2012, 33(6): 96−99. doi: 10.3969/j.issn.1003-5508.2012.06.022
[18]	Martell D L, Otukol S, Stocks B J. A logistic model for predicting daily people-caused forest fire occurrence in Ontario[J]. Canadian Journal of Forest Research, 1987, 17(5): 394−401. doi: 10.1139/x87-068
[19]	苏漳文, 刘爱琴, 郭福涛, 等. 福建林火发生的驱动因子及空间格局分析[J]. 自然灾害学报, 2016, 25(2):110−119. Su Z W, Liu A Q, Guo F T, et al. Driving factors and spatial distribution pattern of forest fire in Fujian Province[J]. Journal of Natural Disasters, 2016, 25(2): 110−119.
[20]	于建龙, 刘乃安. 我国大兴安岭地区森林雷击火发生的火险天气等级研究[J]. 火灾科学, 2010, 19(3):131−137. doi: 10.3969/j.issn.1004-5309.2010.03.004 Yu J L, Liu N A. Lightning-caused wildland fire weather danger rating in Daxing’anling region[J]. Fire Safety Science, 2010, 19(3): 131−137. doi: 10.3969/j.issn.1004-5309.2010.03.004
[21]	Bisquert M, Caselles E, Sánchez J M, et al. Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data[J]. International Journal of Wildland Fire, 2012, 21(8): 1025−1029. doi: 10.1071/WF11105
[22]	Oliveira S, Oehler F, San-Miguel-Ayanz J, et al. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest[J]. Forest Ecology and Management, 2012, 275(4): 117−129.
[23]	陈岱. 基于Logistic回归模型的大兴安岭林火预测研究[J]. 林业资源管理, 2019(1):116−122. Chen D. Prediction of forest fire occurrence in Daxing’an Mountains based on logistic regression model[J]. Forest Resources Management, 2019(1): 116−122.
[24]	Raftery A E, Gneiting T, Balabdaoui F, et al. Using Bayesian model averaging to calibrate forecast ensembles[J]. Monthly Weather Review, 2005, 133(5): 1155−1174. doi: 10.1175/MWR2906.1
[25]	梁慧玲, 林玉蕊, 杨光, 等. 基于气象因子的随机森林算法在塔河地区林火预测中的应用[J]. 林业科学, 2016, 52(1):89−98. Liang H L, Lin Y R, Yang G, et al. Application of random forest algorithm on the forest fire prediction in Tahe Area based on meteorological factors[J]. Scientia Silvae Sinicae, 2016, 52(1): 89−98.
[26]	顾先丽, 吴志伟, 张宇婧, 等. 气候变化背景下江西省林火空间预测[J]. 生态学报, 2020, 40(2):667−677. Gu X L, Wu Z W, Zhang Y J, et al. Prediction research of the forest fire in Jiangxi Province in the background of climate change[J]. Acta Ecological Sinica, 2020, 40(2): 667−677.
[27]	Chang Y, Zhu Z L, Bu R C, et al. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China[J]. Landscape Ecology, 2013, 28(10): 1989−2004. doi: 10.1007/s10980-013-9935-4
[28]	Guo F T, Su Z W, Wang G Y, et al. Understanding fire drivers and relative impacts in different Chinese forest ecosystems[J]. Science of the Total Environment, 2017, 605: 411−425.
[29]	Flannigan M D, Krawchuk M A, Groot W J D, et al. Implications of changing climate for global wildland fire[J]. International Journal of Wildland Fire, 2009, 18(5): 483−507. doi: 10.1071/WF08187
[30]	Loepfe L, Rodrigo A, Lloret F. Two thresholds determine climatic control of forest fire size in Europe and northern Africa[J]. Regional Environmental Change, 2014, 14(4): 1395−1404. doi: 10.1007/s10113-013-0583-7
[31]	蔡奇均, 曾爱聪, 苏漳文, 等. 基于Logistic回归模型的浙江省林火发生驱动因子分析[J]. 西北农林科技大学学报, 2020, 48(2):108−115. Cai Q J, Zeng A C, Su Z W, et al. Driving factors of forest fire in Zhejiang Province based on logistic regression model[J]. Journal of Northwest A&F University, 2020, 48(2): 108−115.

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Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example