Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm

Ecological Informatics - Tập 63 - Trang 101292 - 2021
Tran Thi Tuyen1, Abolfazl Jaafari2, Hoang Phan Hải Yen3, T. Nguyen‐Thoi4,5, Tran Van Phong6, Huu Duy Nguyen7, Hiep Van Le8, Tran Thi Mai Phuong9, Son Hoang Nguyen10, Indra Prakash11, Binh Thai Phạm8
1Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Viet Nam
2Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran
3Department of Geography, School of Social Education, Vinh University, Viet Nam
4Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 700000, Viet Nam
5Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh 700000, Viet Nam
6Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, Viet Nam
7Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Ha Noi, Viet Nam
8University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam
9Hanoi University of Natural Resources and Environment, No 41A, Phu Dien street, Bac Tu Liem district, Hanoi City, Viet Nam
10Open Education and Information Technology, Hue University, No. 05 Ha Noi Street, Hue City, Viet Nam
11DDG (R) Geological Survey of India, Gandhinagar 382010, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Abedi Gheshlaghi, 2021, Forest fire susceptibility modeling using hybrid approaches, Trans. GIS, 25, 311, 10.1111/tgis.12688

Abedini, 2019, A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment, Geocarto Int., 34, 1427, 10.1080/10106049.2018.1499820

Abrams, 2019, Global change impacts on forest and fire dynamics using paleoecology and tree census data for eastern North America, Ann. For. Sci., 76, 8, 10.1007/s13595-018-0790-y

Angayarkkani, 2011, An effective technique to detect forest fire region through ANFIS with spatial data, 24

Arif, 2001, Incorporation of experience in iterative learning controllers using locally weighted learning, Automatica, 37, 881, 10.1016/S0005-1098(01)00030-9

Atkeson, 1997, 11

Bai, 2019, Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial, Futur. Gener. Comput. Syst., 93, 1036, 10.1016/j.future.2018.04.021

Barlow, 2003, Large tree mortality and the decline of forest biomass following Amazonian wildfires, Ecol. Lett., 6, 6, 10.1046/j.1461-0248.2003.00394.x

Bergeron, 2001, Natural fire frequency for the eastern Canadian boreal forest: consequences for sustainable forestry, Can. J. For. Res., 31, 384, 10.1139/x00-178

Boer, 2020, Unprecedented burn area of Australian mega forest fires, Nat. Clim. Chang., 10, 171, 10.1038/s41558-020-0716-1

Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655

Bush, 2016, A 6900-year history of landscape modification by humans in lowland Amazonia, Quat. Sci. Rev., 141, 52, 10.1016/j.quascirev.2016.03.022

Cai, 2019, Learning complexity-aware cascades for pedestrian detection, IEEE Trans. Pattern Anal. Mach. Intell., 42, 2195, 10.1109/TPAMI.2019.2910514

Cao, 2019, Multiobjective 3-D topology optimization of next-generation wireless data center network, IEEE Trans. Ind. Inform., 16, 3597, 10.1109/TII.2019.2952565

Cao, 2020, Applying graph-based differential grouping for multiobjective large-scale optimization, Swarm Evol. Comput., 53, 100626, 10.1016/j.swevo.2019.100626

Carmo, 2011, Land use and topography influences on wildfire occurrence in northern Portugal, Landsc. Urban Plan., 100, 169, 10.1016/j.landurbplan.2010.11.017

Chakraborty, 2019

Chao, 2018, Geographically weighted regression based methods for merging satellite and gauge precipitation, J. Hydrol., 558, 275, 10.1016/j.jhydrol.2018.01.042

Chen, 2020, GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models, CATENA, 195, 104777, 10.1016/j.catena.2020.104777

Chen, 2019, A fuzzy optimization strategy for the implementation of RBF LSSVR model in vis–NIR analysis of pomelo maturity, IEEE Trans. Ind. Inform., 15, 5971, 10.1109/TII.2019.2933582

Choi, 2020, An ensemble learner-based bagging model using past output data for photovoltaic forecasting, Energies, 13, 1438, 10.3390/en13061438

da Silva, 2018, Dynamics of forest fires in the southwestern Amazon, For. Ecol. Manag., 424, 312, 10.1016/j.foreco.2018.04.041

Dimitrakopoulos, 2001, Flammability assessment of Mediterranean forest fuels, Fire. Technol, 37, 143, 10.1023/A:1011641601076

Dong, 2020, Reliability and availability analysis of stochastic degradation systems based on bivariate Wiener processes, Appl. Math. Model., 79, 414, 10.1016/j.apm.2019.10.044

Elia, 2020, Likelihood and frequency of recurrent fire ignitions in highly urbanised Mediterranean landscapes, Int. J. Wildland Fire, 29, 120, 10.1071/WF19070

Feng, 2020, Drought characteristics and its elevation dependence in the Qinghai–Tibet plateau during the last half-century, Sci. Rep., 10, 1, 10.1038/s41598-020-71295-1

Fernández-García, 2020, Do fire regime attributes affect soil biochemical properties in the same way under different environmental conditions?, Forests, 11, 274, 10.3390/f11030274

Flannigan, 2009, Implications of changing climate for global wildland fire, Int. J. Wildland Fire, 18, 483, 10.1071/WF08187

Francos, 2020, Long-term forest management after wildfire (Catalonia, NE Iberian Peninsula), J. For. Res., 31, 269, 10.1007/s11676-018-0867-3

Gama, 2000, Cascade generalization, Mach. Learn., 41, 315, 10.1023/A:1007652114878

García-Llamas, 2019, Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems, For. Ecol. Manag., 433, 24, 10.1016/j.foreco.2018.10.051

Gholamnia, 2020, Comparisons of diverse machine learning approaches for wildfire susceptibility mapping, Symmetry, 12, 604, 10.3390/sym12040604

Gibson, 2020, A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest, Remote Sens. Environ., 240, 111702, 10.1016/j.rse.2020.111702

Goldarag, 2016, Fire risk assessment using neural network and logistic regression, J. Ind. Soc. Remote Sens., 44, 885, 10.1007/s12524-016-0557-6

Goleiji, 2017, Forest fire risk assessment-an integrated approach based on multicriteria evaluation, Environ. Monit. Assess., 189, 612, 10.1007/s10661-017-6225-7

Gralewicz, 2012, Factors influencing national scale wildfire susceptibility in Canada, For. Ecol. Manag., 265, 20, 10.1016/j.foreco.2011.10.031

Guo, 2017, Effects of topography and spatial processes on structuring tree species composition in a diverse heterogeneous tropical karst seasonal rainforest, Flora, 231, 21, 10.1016/j.flora.2017.04.002

Han, 2019, Spatially distributed crop model based on remote sensing, Agric. Water Manag., 218, 165, 10.1016/j.agwat.2019.03.035

He, 2018, Ecological vulnerability assessment for ecological conservation and environmental management, J. Environ. Manag., 206, 1115, 10.1016/j.jenvman.2017.11.059

Hirsch, 2001, Fire-smart forest management: a pragmatic approach to sustainable forest management in fire-dominated ecosystems, For. Chron., 77, 357, 10.5558/tfc77357-2

Hong, 2019, Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: an integrated model to analysis of landscape indicators, Ecol. Indic., 101, 878, 10.1016/j.ecolind.2019.01.056

Hosmer, 2013

Jaafari, 2018, LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process, Environ. Earth Sci., 77

Jaafari, 2019, 607

Jaafari, 2017, A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran, Ecol. Inform., 39, 32, 10.1016/j.ecoinf.2017.03.003

Jaafari, 2018, Wildfire spatial pattern analysis in the Zagros Mountains, Iran: a comparative study of decision tree based classifiers, Ecol. Inform., 43, 200, 10.1016/j.ecoinf.2017.12.006

Jaafari, 2019, Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability, J. Environ. Manag., 243, 358, 10.1016/j.jenvman.2019.04.117

Jiang, 2013, Naive Bayes text classifiers: a locally weighted learning approach, J. Exp. Theor. Artif. Intell., 25, 273, 10.1080/0952813X.2012.721010

Karnieli, 2010, Use of NDVI and land surface temperature for drought assessment: Merits and limitations, J. Clim., 23, 618, 10.1175/2009JCLI2900.1

Kelsey, 2019

Kim, 2019, Multi-temporal analysis of forest fire probability using socio-economic and environmental variables, Remote Sens., 11, 10.3390/rs11010086

Kuhn, 2013

Kuuluvainen, 2012, Natural disturbance emulation in boreal forest ecosystem management—theories, strategies, and a comparison with conventional even-aged management, Can. J. For. Res., 42, 1185, 10.1139/x2012-064

Li, 2018, Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies, Biochim. Biophys. Acta (BBA) - Mol. Basis Dis., 1864, 2241, 10.1016/j.bbadis.2017.10.036

Li, 2019, A deep learning approach for multi-frame in-loop filter of HEVC, IEEE Trans. Image Process., 28, 5663, 10.1109/TIP.2019.2921877

Li, 2020, Distributive features of soil carbon and nutrients in permafrost regions affected by forest fires in northern Da Xing’anling (Hinggan) Mountains, NE China, Catena, 185, 104304, 10.1016/j.catena.2019.104304

Liang, 2019, A neural network model for wildfire scale prediction using meteorological factors, IEEE Access, 7, 176746, 10.1109/ACCESS.2019.2957837

Lima, 2015, Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation, Environ. Model Softw., 73, 175, 10.1016/j.envsoft.2015.08.002

Liu, 2020, Exploring factors influencing construction waste reduction: A structural equation modeling approach, J. Clean. Prod., 276, 123185, 10.1016/j.jclepro.2020.123185

Lu, 2020, Patch aggregation trends of the global climate landscape under future global warming scenario, Int. J. Climatol., 40, 2674, 10.1002/joc.6358

Lv, 2020, Deep belief network and linear perceptron based cognitive computing for collaborative robots, Appl. Soft Comput., 92, 106300, 10.1016/j.asoc.2020.106300

Ma, 2020, Identifying forest fire driving factors and related impacts in china using random forest algorithm, Forests, 11, 507, 10.3390/f11050507

Mafi-Gholami, 2019, Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran, Sci. Total Environ., 656, 1326, 10.1016/j.scitotenv.2018.11.462

Massada, 2013, Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA, Int. J. Wildland Fire, 22, 174, 10.1071/WF11178

McCune, 2002

Melville, 2005, Creating diversity in ensembles using artificial data, Inform. Fusion, 6, 10.1016/j.inffus.2004.04.001

Milanović, 2021, Forest fire probability mapping in Eastern Serbia: logistic regression versus random forest method, Forests, 12, 5, 10.3390/f12010005

Moayedi, 2020, Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility, J. Environ. Manag., 260, 109867, 10.1016/j.jenvman.2019.109867

Mosavi, 2018, Flood prediction using machine learning models: literature review, Water, 10, 1536, 10.3390/w10111536

Mousavi, 2019, Constructing cascade bloom filters for efficient access enforcement, Comput. Secur., 81, 1, 10.1016/j.cose.2018.09.015

Nami, 2018, Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS, Int. J. Environ. Sci. Technol., 15, 373, 10.1007/s13762-017-1371-6

Nolan, 2020, Causes and consequences of eastern Australia’s 2019–20 season of mega-fires, Global Change Biol., 26, 1039, 10.1111/gcb.14987

Parente, 2019, Drought in Portugal: current regime, comparison of indices and impacts on extreme wildfires, Sci. Total Environ., 685, 150, 10.1016/j.scitotenv.2019.05.298

Parisien, 2016, The spatially varying influence of humans on fire probability in North America, Environ. Res. Lett., 11, 10.1088/1748-9326/11/7/075005

Parisien, 2020, Applications of simulation-based burn probability modelling: a review, Int. J. Wildland Fire, 28, 913, 10.1071/WF19069

Parkins, 2018, Edge effects in fire-prone landscapes: ecological importance and implications for fauna, Ecol. Evol., 8, 5937, 10.1002/ece3.4076

Pham, 2019, Hybrid computational intelligence models for groundwater potential mapping, Catena, 182, 10.1016/j.catena.2019.104101

Pham, 2020, Performance evaluation of machine learning methods for forest fire modeling and prediction, Symmetry, 12, 1022, 10.3390/sym12061022

Pourreza, 2014, Soil microbial activity in response to fire severity in Zagros oak (Quercus brantii Lindl.) forests, Iran, after one year, Geoderma, 213, 95, 10.1016/j.geoderma.2013.07.024

Rahmati, 2019, Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia, Sci. Total Environ., 718

Razavi-Termeh, 2020, Ubiquitous GIS-based forest fire susceptibility mapping using artificial intelligence methods, Remote Sens., 12, 1689, 10.3390/rs12101689

Renard, 2012, Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India, Int. J. Wildland Fire, 21, 368, 10.1071/WF10109

Reyes, 2018, A locally weighted learning method based on a data gravitation model for multi-target regression, Int. J. Comput. Intel. Syst., 11, 282, 10.2991/ijcis.11.1.22

Ricotta, 2014, Modeling the landscape drivers of fire recurrence in Sardinia (Italy), Environ. Manag., 53, 1077, 10.1007/s00267-014-0269-z

Rodrigues, 2018, A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using geographically weighted logistic regression, J. Environ. Manag., 225, 177, 10.1016/j.jenvman.2018.07.098

Sevinc, 2020, A Bayesian network model for prediction and analysis of possible forest fire causes, For. Ecol. Manag., 457, 117723, 10.1016/j.foreco.2019.117723

Sun, 2017, Composite adaptive locally weighted learning control for multi-constraint nonlinear systems, Appl. Soft Comput., 61, 1098, 10.1016/j.asoc.2017.09.011

Sun, 2019, An adaptive differential evolution with combined strategy for global numerical optimization, Soft Comput., 1

Tewari, 2019, Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs, Comput. Ind. Eng., 128, 937, 10.1016/j.cie.2018.08.018

Thach, 2018, Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: a comparative study, Ecol. Inform., 46, 74, 10.1016/j.ecoinf.2018.05.009

Ting, 1997

Tran, 2020, Novel ensemble landslide predictive models based on the hyperpipes algorithm: a case study in the Nam Dam Commune, Vietnam, Appl. Sci., 10

Vecín-Arias, 2016, Biophysical and lightning characteristics drive lightning-induced fire occurrence in the central plateau of the Iberian Peninsula, Agric. For. Meteorol., 225, 36, 10.1016/j.agrformet.2016.05.003

Veronesi, 2019, Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation, Ecol. Indic., 101, 1032, 10.1016/j.ecolind.2019.02.026

Vicente-Serrano, 2019, A review of environmental droughts: increased risk under global warming?, Earth Sci. Rev., 201, 1

Viedma, 2018, Wildfires and the role of their drivers are changing over time in a large rural area of west-central Spain, Sci. Rep., 8, 17797, 10.1038/s41598-018-36134-4

Viedma, 2020, Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain, Remote Sens. Environ., 247, 10.1016/j.rse.2020.111891

Vilar, 2010, A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain, Int. J. Wildland Fire, 19, 325, 10.1071/WF09030

Wang, 2020, Physically-based landslide prediction over a large region: scaling low-resolution hydrological model results for high-resolution slope stability assessment, Environ. Model Softw., 124, 104607, 10.1016/j.envsoft.2019.104607

Wei, 2019, Methods to detect edge effected reductions in fire frequency in simulated forest landscapes, ISPRS Int. J. Geo Inform., 8, 10.3390/ijgi8060277

Wolpert, 1992, Stacked generalization, Neural Netw., 5, 241, 10.1016/S0893-6080(05)80023-1

Wu, 2015, Defining fire environment zones in the boreal forests of northeastern China, Sci. Total Environ., 518, 106, 10.1016/j.scitotenv.2015.02.063

Wu, 2020, Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping, CATENA, 187, 104396, 10.1016/j.catena.2019.104396

Wu, 2020, Current and future patterns of forest fire occurrence in China, Int. J. Wildland Fire, 29, 104, 10.1071/WF19039

Zhang, 2019, Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China, Int. J. Disast. Risk Sci., 1

Zhang, 2020, A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: a case study in a typical semi-arid North China basin, Environ. Model Softw., 128, 104704, 10.1016/j.envsoft.2020.104704

Zhao, 2020, GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques, Appl. Sci., 10, 16, 10.3390/app10010016

Zhu, 2019, Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach, Autom. Constr., 106, 102859, 10.1016/j.autcon.2019.102859