Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240400177-8.doi: 10.11896/jsjkx.240400177

• Artificial Intelligence • Previous Articles     Next Articles

Research Progress and Challenges in Forest Fire Risk Prediction

YANG Jixiang, JIANG Huiping, WANG Sen, MA Xuan   

  1. Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE,Minzu University of China,Beijing 100081,China
    School of Information Engineering,Minzu University of China,Beijing 100071,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:YANG Jixiang,born in 2000,postgra-duate.His main research interests include computer applications and remote sensing image processing.
    JIANG Huiping,born in 1975,Ph.D,professor,Ph.D supervisor.Her main research interests include artificial intelligence,affective computing and intelligent education.

Abstract: With the intensification of global climate change and human activities,forest fire incidents have become increasingly frequent,leading to severe ecological damage and socioeconomic losses.Forest fire risk prediction,as a primary measure for forest fire management and monitoring,has significant importance.Therefore,this study conducts an in-depth analysis of existing forest fire risk prediction methods.These methods are categorized into three types based on different data sources:models based on geographical environmental factors,models based on remote sensing and geographic information systems(GIS),and models based on remote sensing imagery.The characteristics of each method are thoroughly summarized,and their research approaches,application scopes,and specific requirements for data and algorithms are analyzed.Subsequently,this study introduces several datasets proposed by relevant researchers in the field of forest fire risk prediction and compares the experimental results of the mentioned prediction methods.Finally,the major issues associated with the three types of models are analyzed,and future research directions are proposed.

Key words: Forest fires, Fire prediction, Remote sensing, Artificial intelligence, Machine learning, Deep learning

CLC Number: 

  • TP399
[1]BAI N,WANG B,WU Y D,et al.Overview of global forest fires in 2021[J].Fire Science and Technology,2022,41(4):705.
[2]WU Y Y,SHU L F,WANG M Y,et al.Overview of forest fires in the world in recent years[J].Temperate Forestry Research,2022,5(4):49-54.
[3]BARMPOUTIS P,PAPAIOANNOU P,DIMITROPOULOS K,et al.A review on early forest fire detection systems using optical remote sensing[J].Sensors,2020,20(22):6442.
[4]HUTCHINSON M,STEIN J,STEIN J,et al.GEODATA 9 s DEM and D8:Digital Elevation Model Version 3 and Flow Direction Grid 2008[EB/OL].http://pid.geoscience.gov.au/dataset/ga/66006.
[5]ABATZOGLOU J T,DOBROWSKI S Z,PARKS S A,et al.TerraClimate,a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015[J].Scientific data,2018,5(1):1-12.
[6]SAYAD Y O,MOUSANNIF H,AL MOATASSIME H J.Predictive modeling of wildfires:A new dataset and machine learning approach[J].Fire Safety Journal,2019:104:130-146.
[7]MONACO S,GRECO S,FARASIN A,et al.Attention to fires:Multi-channel deep learning models for wildfire severity prediction[J].Applied Sciences,2021,11(22):11060.
[8]SINGLA S,MUKHOPADHYAY A,WILBURM,et al.Wild-firedb:An open-source dataset connecting wildfire occurrence with relevant determinants[C]//NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems.2021.
[9]HU X,ZHANG P,BAN Y.Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models[J].ISPRS Journal of Photogrammetry and Remote Sensing,2023,196:228-240.
[10]HUOT F,HU R L,GOYAL N,et al.Next day wildfire spread:A machine learning dataset to predict wildfire spreading from remote-sensing data[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-13.
[11]SHEN S,SENEVIRATNE S,WANYAN X.Firerisk:A remote sensing dataset for fire risk assessment with benchmarks using supervised and self-supervised learning[C]//International Conference on Digital Image Computing:Techniques and Applications(DICTA).IEEE.2023:189-196.
[12]JAISWAL R K,MUKHERJEE S,RAJU K D,et al.Forest fire risk zone mapping from satellite imagery and RS[J].International journal of applied earth observation and geoinformation,2002,4(1):1-10.
[13]GUO F,SU Z,WANG G,et al.Wildfire ignition in the forests of southeast China:Identifying drivers and spatial distribution to predict wildfire likelihood[J].Applied Geography,2016,66:12-21.
[14]STOCKS B J,LAWSON B D,ALEXANDER M E,et al.The Canadian forest fire danger rating system:an overview[J].The Forestry Chronicle,1989,65(6):450-457.
[15]FINNEY M A,MCHUGH C W,GRENFELL I C,et al.A simulation of probabilistic wildfire risk components for the continental United States[J].Stochastic Environmental Research and Risk Assessment,2011,25:973-1000.
[16]SIMARD S J.Fire severity,changing scales,and how thingshang together[J].International Journal of Wildland Fire,1991,1(1):23-34.
[17]QU J,CUI X.Automatic machine learning framework for forest fire forecasting[C]//Journal of Physics:Conference Series.IOP Publishing,2020,1651(1):012116.
[18]LIN H,LIU X,WANG X,et al.A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks[J].Sustainable Computing:Informatics and Systems,2018,18:101-111.
[19]BUI D T,VAN LE H,HOANG N D.RS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method[J].Ecological Informatics,2018,48:104-116.
[20]GAO B,SHAN Y,LIU X et al.Prediction and driving factors of forest fire occurrence in Jilin Province,China[J].Journal of Forestry Research,2024,35(21):21-26.
[21]LI D.Study on the relationship between forest fires and meteorological factors in key areas of Sichuan Province[D].Beijing:Beijing Forestry University,2013.
[22]ADHIKARI D,CHEN W,GUO Y,et al.Wildfire ProgressionPrediction and Validation Using Satellite Data and Remote Sensing in Sonoma,California[C]//2023 IEEE International Conference on Service-Oriented System Engineering(SOSE).IEEE,2023:262-271.
[23]LIANG H,ZHANG M,WANG H.A neural network model for wildfire scale prediction using meteorological factors[J].IEEE Access,2019,7:176746-176755.
[24]CHANG Y,ZHU Z,BU R,et al.Predicting fire occurrence patterns with loRStic regression in Heilongjiang Province,China[J].Landscape Ecology,2013,28(10):1989-2004.
[25]PAN J,WANG W,LI J.Building probabilistic models of fire oc-currence and fire risk zoning using loRStic regression in Shanxi Province,China[J].Natural Hazards,2016,81(3):1879-1899.
[26]SURYABHAGAVAN K V,ALEMU M,BALAKRISHNANM.RS-based multi-criteria decision analysis for forest fire susceptibility mapping:a case study in Harenna forest,southwestern Ethiopia[J].Tropical Ecology,2016,57(1):33-43.
[27]HUESCA M,LITAGO J,PALACIOS-ORUETA A,et al.As-sessment of forest fire seasonality using MODIS fire potential:A time series approach[J].Agricultural and Forest Meteorology,2009,149(11):1946-1955.
[28]JAISWAL R K,MUKHERJEE S,RAJU K D,et al.Forest fire risk zone mapping from satellite imagery and RS[J].International Journal of Applied Earth Observation and Geoinformation,2002,4(1):1-10.
[29]SAYAD Y O,MOUSANNIF H,AL MOATASSIME H.Predictive modeling of wildfires:A new dataset and machine learning approach[J].Fire safety journal,2019,104:130-146.
[30]MALIK A,JALIN N,RANI S,et al.Wildfire Risk Prediction and Detection using Machine Learning in San Diego,California[C]//2021 IEEE SmartWorld,Ubiquitous Intelligence & Computing,Advanced & Trusted Computing,Scalable Computing & Communications,Internet of People and Smart City Innovation(SmartWorld/SCALCOM/UIC/ATC/IOP/SCI).IEEE,2021:622-629.
[31]KAUR P.Forest fire prediction using heterogeneous datasources and machine learning methods[D].University of Waterloo,2023.
[32]YANG S,LUPASCU M,MEEL K S.Predicting forest fire using remote sensing data and machine learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:14983-14990.
[33]FIRMS N.Fire information for resource management system[EB/OL].https://firms.modaps.eosdis.nasa.gov/map.
[34]RUBÍ J N S,DE CARVALHO P H P,GONDIM P R L.Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region[J].Engineering Applications of Artificial Intelligence,2023,118:105649.
[35]DE VASCONCELOS M J P,SILVA S,TOMEM,et al.Spatial prediction of fire ignition probabilities:comparing loRStic regression and neural networks[J].Photogrammetric engineering and remote sensing,2001,67(1):73-81.
[36]TUYEN T T,JAAFARI A,YEN H P H,et al.Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm[J].Ecological Informatics,2021,63:101292.
[37]CHEN X F,LIU L,LI J G,et al.Application and research progress of satellite remote sensing fire point monitoring[J].Journal of Remote Sensing,2020,24(5).
[38]POURGHASEMI H R,GAYEN A,LASAPONARA R,et al.Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling[J].Environmental research,2020,184:109321.
[39]SHMUEL A,HEIFETZ E.Global wildfire susceptibility mapping based on machine learning models[J].Forests,2022,13(7):1050.
[40]ZHANG L,WANG M,DING Y,et al.FBC-ANet:A semantic segmentation model for UAV forest fire images combining boundary enhancement and context awareness[J].Drones,2023,7(7):456.
[41]ABDUSALOMOV A B,ISLAM B M D S,NASIMOV R,et al.An improved forest fire detection method based on the detectron2 model and a deep learning approach[J].Sensors,2023,23(3):1512.
[42]HODGES J L,LATTIMER B Y.Wildland fire spread modeling using convolutional neural networks[J].Fire technology,2019,55:2115-2142.
[43]LIN X,LI Z,CHEN W,et al.Forest fire prediction based onlong-and short-term time-series network[J].Forests,2023,14(4):778.
[44]MIAO X,LI J,MU Y,et al.Time Series Forest Fire Prediction Based on Improved Transformer[J].Forests,2023,14(8):1596.
[45]DENG J,DONG W,SOCHER R,et al.Imagenet:A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:248-255.
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