Prediction and driving factors of forest fire occurrence in Jilin Province, China

Bo Gao, Yanlong Shan, Xiangyu Liu, Sainan Yin, Bo Yu, Chenxi Cui, Lili Cao

Journal of Forestry Research ›› 2023, Vol. 35 ›› Issue (1) : 21.

Journal of Forestry Research All Journals
PDF
Journal of Forestry Research ›› 2023, Vol. 35 ›› Issue (1) : 21. DOI: 10.1007/s11676-023-01663-w
Original Paper

Prediction and driving factors of forest fire occurrence in Jilin Province, China

Author information +
History +

Abstract

Forest fires are natural disasters that can occur suddenly and can be very damaging, burning thousands of square kilometers. Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model, the geographical weighted logistic regression model, the Lasso regression model, the random forest model, and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province. The models, along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area. Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models. The accuracies of the random forest model, the support vector machine model, geographical weighted logistic regression model, the Lasso regression model, and logistic model were 88.7%, 87.7%, 86.0%, 85.0% and 84.6%, respectively. Weather is the main factor affecting forest fires, while the impacts of topography factors, human and social-economic factors on fire occurrence were similar.

Cite this article

Download citation ▾
Bo Gao, Yanlong Shan, Xiangyu Liu, Sainan Yin, Bo Yu, Chenxi Cui, Lili Cao. Prediction and driving factors of forest fire occurrence in Jilin Province, China. Journal of Forestry Research, 2023, 35(1): 21 https://doi.org/10.1007/s11676-023-01663-w
This is a preview of subscription content, contact us for subscripton.

References

[]
AndrewsPL. Current status and future needs of the behaveplus fire modeling system. Int J Wildland Fire, 2014, 23(1): 21-33
CrossRef Google scholar
[]
AndrewsPL. The Rothermel surface fire spread model and associated developments: a comprehensive explanation. Gen Tech Rep, 2018, 121: 371
[]
AndrewsPL, LoftsgaardenDO, BradshawLS. Evaluation of fire danger rating indexes using logistic regression and percentile analysis. Int J Wildland Fire, 2003, 12(2): 213-226
CrossRef Google scholar
[]
BisquertM, CasellesE, SánchezJM, CasellesV. Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. Int J Wildland Fire, 2012, 21(8): 1025-1029
CrossRef Google scholar
[]
BoubetaM, LombardíaMJ, Marey-PérezM, MoralesD. Poisson mixed models for predicting number of fires. Int J Wildland Fire, 2019, 28(3): 237-253
CrossRef Google scholar
[]
BreimanL. Random forests. Mach Learn, 2001, 45: 5-32
CrossRef Google scholar
[]
CardilleJA, VenturaSJ, TurnerMG. Environmental and social factors influencing wildfires in the Upper Midwest. United States Ecol Appl, 2001, 11(1): 111-127
CrossRef Google scholar
[]
CatryFX, RegoFC, BaçãoFL, MoreiraF. Modeling and mapping wildfire ignition risk in Portugal. Int J Wildland Fire, 2009, 18(8): 921-931
CrossRef Google scholar
[]
CawsonJG, DuffTJ. Forest fuel bed ignitability under marginal fire weather conditions in Eucalyptus forests. Int J Wildland Fire, 2019, 28(3): 198-204
CrossRef Google scholar
[]
CortesC, VapnikV. Support-vector networks. Mach Learn, 1995, 20: 273-297
CrossRef Google scholar
[]
CoughlanR, Di GiuseppeF, VitoloC, BarnardC, LopezP, DruschM. Using machine learning to predict fire-ignition occurrences from lightning forecasts. Meteorol Appl, 2021, 28(1): e1973
CrossRef Google scholar
[]
Díaz-AvalosC, PetersonDL, AlvaradoE, FergusonSA, BesagJE. Space time modelling of lightning-caused ignitions in the Blue Mountains. Oregon Can J Forest Res, 2001, 31(9): 1579-1593
[]
EliaM, D'EsteM, AscoliD, GiannicoV, SpanoG, GangaA, ColangeloG, LafortezzaR, SanesiG. Estimating the probability of wildfire occurrence in Mediterranean landscapes using artificial neural networks. Environ Impact Assess Rev, 2020, 85: 106474
CrossRef Google scholar
[]
EslamiR, AzarnoushM, KialashkiA, KazemzadehF. Gis-based forest fire susceptibility assessment by random forest, artificial neural network and logistic regression methods. J Trop for Sci, 2021, 33(2): 173-184
[]
Finney MA (1998). FARSITE, Fire Area Simulator--model development and evaluation: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.
[]
GhoshD, ChinnaiyanAM. Classification and selection of biomarkers in genomic data using LASSO. J Biomed Biotechnol, 2005, 2: 147
[]
GibsonRK, BradstockRA, PenmanT, KeithDA, DriscollDA. Climatic, vegetation and edaphic influences on the probability of fire across mediterranean woodlands of south-eastern Australia. J Biogeogr, 2015, 42(9): 1750-1760
CrossRef Google scholar
[]
GigovićL, PourghasemiHR, DrobnjakS, BaiS. Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National park. Forests, 2019, 10(5): 408
CrossRef Google scholar
[]
GuoF, SuZ, WangG, SunL, LinF, LiuA. Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood. Appl Geogr, 2016, 66: 12-21
CrossRef Google scholar
[]
GuoF, WangG, SuZ, LiangH, WangW, LinF, LiuA. What drives forest fire in Fujian, China? Evidence from logistic regression and random forests. Int J Wildland Fire, 2016, 25(5): 505-519
CrossRef Google scholar
[]
GuoF, SuZ, WangG, SunL, TigabuM, YangX, HuH. Understanding fire drivers and relative impacts in different Chinese forest ecosystems. Sci Total Environ, 2017, 605: 411-425
CrossRef Google scholar
[]
GuoE, ZhangJ, WangY, AluS, WangR, LiD, HaS. Assessing non-linear variation of temperature and precipitation for different growth periods of maize and their impacts on phenology in the Midwest of Jilin Province. China Theor Appl Climtol, 2018, 132(3): 685-699
CrossRef Google scholar
[]
HardyCC, HardyCE. Fire danger rating in the United States of America: an evolution since 1916. Int J Wildland Fire, 2007, 16(2): 217-231
CrossRef Google scholar
[]
JollyWM, CochraneMA, FreebornPH, HoldenZA, BrownTJ, WilliamsonGJ, BowmanDM. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun, 2015, 6(1): 1-11
CrossRef Google scholar
[]
KeeleyJE, SyphardAD. Large California wildfires: 2020 fires in historical context. Fire Ecol, 2021, 17(1): 1-11
CrossRef Google scholar
[]
KimSJ, LimCH, KimGS, LeeJ, GeigerT, RahmatiO, SonY, LeeWK. Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sens, 2019, 11(1): 86
CrossRef Google scholar
[]
KreyeJK, HiersJK, VarnerJM, HornsbyB, DrukkerS, O’brien JJ, . Effects of solar heating on the moisture dynamics of forest floor litter in humid environments: composition, structure, and position matter. Can J Forest Res, 2018, 48(11): 1331-1342
CrossRef Google scholar
[]
LiY, WangYF, SunYJ, LeiYC, ShaoWC, LiJ. Temporal-spatial characteristics of NPP and its response to climate change of Larix forests in Jilin Province. Acta Ecol Sin, 2022, 42: 947-959
[]
LuB, HarrisP, CharltonM, BrunsdonC. The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-Spatial Inf Sci, 2014, 17(2): 85-101
CrossRef Google scholar
[]
MaW, FengZ, ChengZ, ChenS, WangF. Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests, 2020, 11(5): 507
CrossRef Google scholar
[]
Martínez-FernándezJ, ChuviecoE, KoutsiasN. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Sys, 2013, 13(2): 311-327
CrossRef Google scholar
[]
MilanovićS, MarkovićN, PamučarD, GigovićL, KostićP, MilanovićSD. Forest fire probability mapping in eastern Serbia: logistic regression versus random forest method. Forests, 2020, 12(1): 5
CrossRef Google scholar
[]
Miranda-AragónL, Treviño-GarzaE, Jiménez-PérezJ, Aguirre-CalderónO, González-TagleM, Pompa-GarcíaM, Aguirre-SaladoC. Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression. J Forestry Res, 2012, 23(3): 345-354
CrossRef Google scholar
[]
MoznyM, TrnkaM, BrázdilR. Climate change driven changes of vegetation fires in the Czech Republic. Theor Appl Clim, 2021, 143(1): 691-699
CrossRef Google scholar
[]
NadeemK, TaylorS, WoolfordDG, DeanC. Mesoscale spatiotemporal predictive models of daily human-and lightning-caused wildland fire occurrence in British Columbia. Int J Wildland Fire, 2019, 29(1): 11-27
CrossRef Google scholar
[]
NurdiatiS, SopaheluwakanA, JuliantoMT, SeptiawanP, RohimahastutiF. Modelling and analysis impact of El Nino and IOD to land and forest fire using polynomial and generalized logistic function: cases study in South Sumatra and Kalimantan. Indonesia Model Earth Syst Environ, 2022, 8(3): 3341-3356
CrossRef Google scholar
[]
NymanP, MetzenD, NoskePJ, LanePN, SheridanGJ. Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel. Int J Wildland Fire, 2015, 24(8): 1129-1142
CrossRef Google scholar
[]
OrdóñezC, SaavedraA, Rodríguez-PérezJR, Castedo-DoradoF, CoviánE. Using model-based geostatistics to predict lightning-caused wildfires. Environ Model Softw, 2012, 29(1): 44-50
CrossRef Google scholar
[]
PetersonD, WangJ, IchokuC, RemerL. Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting. Atmos Chem Phys, 2010, 10(14): 6873-6888
CrossRef Google scholar
[]
PhamBT, JaafariA, AvandM, Al-AnsariN, Du DinhT, YenHPH, PhongTV, NguyenDH, LeHV, Mafi-GholamiD. Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 2020, 12(6): 1022
CrossRef Google scholar
[]
PhelpsN, WoolfordDG. Guidelines for effective evaluation and comparison of wildland fire occurrence prediction models. Int J Wildland Fire, 2021, 30(4): 225-240
CrossRef Google scholar
[]
RanstamJ, CookJ. LASSO regression. Br J Surg, 2018, 105(10): 1348-1348
CrossRef Google scholar
[]
RijalB. Quantile regression: an alternative approach to modelling forest area burned by individual fires. Int J Wildland Fire, 2018, 27(8): 538-549
CrossRef Google scholar
[]
RodriguesM, de la RivaJ, FotheringhamS. Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl Geogr, 2014, 48: 52-63
CrossRef Google scholar
[]
RodriguesM, Costafreda-AumedesS, ComasC, Vega-GarcíaC. Spatial stratification of wildfire drivers towards enhanced definition of large-fire regime zoning and fire seasons. Sci Total Environ, 2019, 689: 634-644
CrossRef Google scholar
[]
RudinC. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 2019, 1(5): 206-215
CrossRef Google scholar
[]
Senande-RiveraM, Insua-CostaD, Miguez-MachoG. Spatial and temporal expansion of global wildland fire activity in response to climate change. Nat Commun, 2022, 13(1): 1-9
CrossRef Google scholar
[]
SharmaLK, GuptaR, FatimaN. Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire. Int J Wildland Fire, 2022, 31(8): 735-758
CrossRef Google scholar
[]
StroblR, GrillE, MansmannU. Graphical modeling of binary data using the LASSO: a simulation study. BMC Med Res Method, 2012, 12(1): 1-13
CrossRef Google scholar
[]
ŠturmT, PodobnikarT. A probability model for long-term forest fire occurrence in the Karst forest management area of Slovenia. Int J Wildland Fire, 2017, 26(5): 399-412
CrossRef Google scholar
[]
SuZ, ZhengL, LuoS, TigabuM, GuoF. Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression. Nat Hazard, 2021, 108(1): 1317-1345
CrossRef Google scholar
[]
TaylorSW, AlexanderME. Science, technology, and human factors in fire danger rating: the Canadian experience. Int J Wildland Fire, 2006, 15(1): 121-135
CrossRef Google scholar
[]
TibshiraniR, SaundersM, RossetS, ZhuJ, KnightK. Sparsity and smoothness via the fused lasso. J R Stat Soc Ser B Stat Methodol, 2005, 67(1): 91-108
CrossRef Google scholar
[]
van der VeldeIR, van der WerfGR, HouwelingS, MaasakkersJD, BorsdorffT, LandgrafJ, TolP, van KempenTA, van HeesR, HoogeveenR. Vast CO2 release from Australian fires in 2019–2020 constrained by satellite. Nature, 2021, 597(7876): 366-369
CrossRef Google scholar
[]
WangZ, LaiC, ChenX, YangB, ZhaoS, BaiX. Flood hazard risk assessment model based on random forest. J Hydrol, 2015, 527: 1130-1141
CrossRef Google scholar
[]
YangG, DiXY, ZengT, ShuZ, WangC, YuHZ. Prediction of area burned under climatic change scenarios: a case study in the Great Xing’an Mountains boreal forest. J Forestry Res, 2010, 21(2): 213-218
CrossRef Google scholar
[]
ZhuY, YuQ, LuoQ, ZhangH, ZhaoJ, JuZ, DuY, YangY. Impacts of climate change on suitability zonation for potato cultivation in Jilin Province. Northeast China Sci Rep, 2021, 11(1): 1-14
Funding
This research was funded by the National Natural Science Foundation of China(32271881)
PDF

100

Accesses

1

Citations

Detail

Sections
Recommended

/