Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

[1]  Wenjing Chen,et al.  Forest Fire Prediction Based on Long- and Short-Term Time-Series Network , 2023, Forests.

[2]  S. V. Razavi-Termeh,et al.  People's olfactory perception potential mapping using a machine learning algorithm: A Spatio-temporal approach , 2023, Sustainable Cities and Society.

[3]  S. V. Razavi-Termeh,et al.  Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery. , 2023, The Science of the total environment.

[4]  Yanfeng Liu,et al.  A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism , 2023, International Journal of Hydrogen Energy.

[5]  Ri-Xue Jin,et al.  Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS , 2022, Remote. Sens..

[6]  Leihong Zhang,et al.  Infrared moving small target detection and tracking algorithm based on feature point matching , 2022, The European Physical Journal D.

[7]  S. V. Razavi-Termeh,et al.  Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India , 2022, Stochastic Environmental Research and Risk Assessment.

[8]  Xufeng Wang,et al.  Classification of Alpine Grasslands in Cold and High Altitudes Based on Multispectral Landsat-8 Images: A Case Study in Sanjiangyuan National Park, China , 2022, Remote. Sens..

[9]  Seyed Vahid Razavi Termeh,et al.  Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Xiangjun Zou,et al.  A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network , 2022, Applied Sciences.

[11]  J. Randerson,et al.  Human-ignited fires result in more extreme fire behavior and ecosystem impacts , 2022, Nature Communications.

[12]  H. Nguyen,et al.  Hybrid models based on deep learning neural network and optimization algorithms for the spatial prediction of tropical forest fire susceptibility in Nghe An province, Vietnam , 2022, Geocarto International.

[13]  Ayyoob Sharifi,et al.  An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran , 2022, Journal of Environmental Planning and Management.

[14]  S. V. Razavi-Termeh,et al.  A spatially based machine learning algorithm for potential mapping of the hearing senses in an urban environment , 2022, Sustainable Cities and Society.

[15]  Weiwei Cai,et al.  PDAM-STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images , 2021, Symmetry.

[16]  Rodrigo De La Fuente,et al.  A deep learning ensemble model for wildfire susceptibility mapping , 2021, Ecol. Informatics.

[17]  Long Sun,et al.  Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind , 2021, Remote. Sens..

[18]  R. Costache,et al.  Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area , 2021 .

[19]  Chao Li,et al.  Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards , 2021, Journal of Hydrology.

[20]  S. V. Razavi-Termeh,et al.  COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms , 2021, International journal of environmental research and public health.

[21]  Tien Giang Nguyen,et al.  A novel combination of deep neural network and Manta ray foraging optimization for flood susceptibility mapping in Quang Ngai province, Vietnam , 2021, Geocarto International.

[22]  Abolghasem Sadeghi-Niaraki,et al.  Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms , 2021, Remote. Sens..

[23]  Binh Thai Pham,et al.  Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm , 2021, Ecol. Informatics.

[24]  Haoyang Yu,et al.  Target-Constrained Interference-Minimized Band Selection for Hyperspectral Target Detection , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Hossein Mojaddadi Rizeei,et al.  Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework , 2021, Remote. Sens..

[26]  Hamid Reza Pourghasemi,et al.  Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing , 2021, ISPRS Int. J. Geo Inf..

[27]  T. Chou,et al.  Henry’s gas solubility optimization algorithm in formulating deep neural network for landslide susceptibility assessment in mountainous areas , 2021, Environmental Earth Sciences.

[28]  H. Pourghasemi,et al.  Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models , 2021, Environmental Science and Pollution Research.

[29]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[30]  H. Leung,et al.  Super-Resolution Mapping Based on Spatial–Spectral Correlation for Spectral Imagery , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Omid Ghorbanzadeh,et al.  Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran , 2021, Geoscience Frontiers.

[32]  Lin Cao,et al.  A Forest Fire Detection System Based on Ensemble Learning , 2021, Forests.

[33]  Preethi Konkathi,et al.  Inter comparison of post-fire burn severity indices of Landsat-8 and Sentinel-2 imagery using Google Earth Engine , 2021, Earth Science Informatics.

[34]  Sébastien Gadal,et al.  A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia , 2020, Remote. Sens..

[35]  Vadim Manusov,et al.  Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting , 2020, Mathematics.

[36]  Bahareh Kalantar,et al.  Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data , 2020, Remote. Sens..

[37]  Jason von Meding,et al.  Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? , 2020 .

[38]  H. Pourghasemi,et al.  Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger , 2020 .

[39]  Gulshan Kumar,et al.  A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning , 2019, Archives of Computational Methods in Engineering.

[40]  Zhongzhi Li,et al.  Wildland Fire Burned Areas Prediction Using Long Short-Term Memory Neural Network with Attention Mechanism , 2020, Fire Technology.

[41]  Hamid Reza Pourghasemi,et al.  A machine learning framework for multi-hazards modeling and mapping in a mountainous area , 2020, Scientific Reports.

[42]  H. Pourghasemi,et al.  Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? , 2020 .

[43]  Sunghae Jun,et al.  Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing , 2020, Applied Sciences.

[44]  Nadhir Al-Ansari,et al.  Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction , 2020, Symmetry.

[45]  Isabelle-Gabriele Hendel,et al.  Efficacy of Remote Sensing in Early Forest Fire Detection: A Thermal Sensor Comparison , 2020, Canadian Journal of Remote Sensing.

[46]  L. Tang,et al.  A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing , 2020, Journal of Forestry Research.

[47]  Soo-Mi Choi,et al.  Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods , 2020, Remote. Sens..

[48]  Biswajeet Pradhan,et al.  Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks , 2020, Remote. Sens..

[49]  H. Shiravand,et al.  A new evaluation of the influence of climate change on Zagros oak forest dieback in Iran , 2020, Theoretical and Applied Climatology.

[50]  J. Nunes,et al.  Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems , 2020 .

[51]  Hossein Moayedi,et al.  Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. , 2020, Journal of environmental management.

[52]  Bahareh Kalantar,et al.  Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification , 2020, Remote. Sens..

[53]  Javad Akbari Torkestani,et al.  An Enhanced Deep Neural Network-Based Architecture for Joint Extraction of Entity Mentions and Relations , 2020, Int. J. Fuzzy Log. Intell. Syst..

[54]  Vladimir F. Krapivin,et al.  A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019 , 2020, Remote. Sens..

[55]  Bakhtiar Feizizadeh,et al.  GIS-based forest fire risk mapping using the analytical network process and fuzzy logic , 2019, Journal of Environmental Planning and Management.

[56]  M. Ali Akcayol,et al.  An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping , 2019, ISPRS Int. J. Geo Inf..

[57]  Yanlong Shan,et al.  Nonparametric multivariate analysis of variance for affecting factors on the extent of forest fire damage in Jilin Province, China , 2019, Journal of Forestry Research.

[58]  H. Pourghasemi,et al.  Multi-hazard probability assessment and mapping in Iran. , 2019, The Science of the total environment.

[59]  Yibin Ying,et al.  Deep learning for vibrational spectral analysis: Recent progress and a practical guide. , 2019, Analytica chimica acta.

[60]  Ming Wang,et al.  Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China , 2019, International Journal of Disaster Risk Science.

[61]  Emilio Chuvieco,et al.  Global Detection of Long-Term (1982-2017) Burned Area with AVHRR-LTDR Data , 2019, Remote. Sens..

[62]  Jeong-Hwan Kim,et al.  Deep learning for multi-year ENSO forecasts , 2019, Nature.

[63]  Dieu Tien Bui,et al.  Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. , 2019, Journal of environmental management.

[64]  Thomas Blaschke,et al.  Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches , 2019, Fire.

[65]  H. Hong,et al.  Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: An integrated model to analysis of landscape indicators , 2019, Ecological Indicators.

[66]  Thomas Blaschke,et al.  Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables , 2019, Fire.

[67]  Pijush Samui,et al.  Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). , 2019, Journal of environmental management.

[68]  Wei Chen,et al.  Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China) , 2019, Bulletin of Engineering Geology and the Environment.

[69]  O. Viedma,et al.  Wildfires and the role of their drivers are changing over time in a large rural area of west-central Spain , 2018, Scientific Reports.

[70]  Ahmed Tealab,et al.  Time series forecasting using artificial neural networks methodologies: A systematic review , 2018, Future Computing and Informatics Journal.

[71]  Marcos Rodrigues,et al.  A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using Geographically Weighted Logistic Regression. , 2018, Journal of environmental management.

[72]  Aman Jantan,et al.  State-of-the-art in artificial neural network applications: A survey , 2018, Heliyon.

[73]  Bang Nguyen Tran,et al.  Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests , 2018, Remote. Sens..

[74]  Francisco Martínez-Álvarez,et al.  A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data , 2018, Theoretical and Applied Climatology.

[75]  Wei Chen,et al.  GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. , 2018, The Science of the total environment.

[76]  Dieu Tien Bui,et al.  Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study , 2018, Ecol. Informatics.

[77]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[78]  Tarunpreet Bhatia,et al.  GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping , 2018, Natural Hazards.

[79]  Mikhail F. Kanevski,et al.  Wildfire susceptibility mapping: Deterministic vs. stochastic approaches , 2018, Environ. Model. Softw..

[80]  Ruiqing Niu,et al.  Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China , 2018, Comput. Geosci..

[81]  A. Jaafari,et al.  Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS , 2018, International Journal of Environmental Science and Technology.

[82]  A. Jaafari LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process , 2018, Environmental Earth Sciences.

[83]  Sung Wook Baik,et al.  Early fire detection using convolutional neural networks during surveillance for effective disaster management , 2017, Neurocomputing.

[84]  S. Monavari,et al.  Forest fire risk assessment-an integrated approach based on multicriteria evaluation , 2017, Environmental Monitoring and Assessment.

[85]  M. Wooster,et al.  Approaches for synergistically exploiting VIIRS I- and M-Band data in regional active fire detection and FRP assessment: A demonstration with respect to agricultural residue burning in Eastern China , 2017 .

[86]  Helmi Zulhaidi Mohd Shafri,et al.  Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data , 2017 .

[87]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[88]  Weibin You,et al.  Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability , 2017 .

[89]  Abolfazl Jaafari,et al.  A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran , 2017, Ecol. Informatics.

[90]  H. Adab Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data , 2017, Natural Hazards.

[91]  Hamid Reza Pourghasemi,et al.  A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China , 2017, Arabian Journal of Geosciences.

[92]  Zhao Yang Dong,et al.  Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report , 2017 .

[93]  Biswajeet Pradhan,et al.  A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area , 2017 .

[94]  W. Schroeder,et al.  Active fire detection using Landsat-8/OLI data , 2016 .

[95]  S. Berberoglu,et al.  Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem , 2016 .

[96]  Biswajeet Pradhan,et al.  Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS , 2016 .

[97]  Evan R. DeLancey,et al.  The spatially varying influence of humans on fire probability in North America , 2016 .

[98]  Peter Berck,et al.  Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California , 2016, PloS one.

[99]  E. Chuvieco,et al.  Global fire size distribution: from power law to log-normal , 2016 .

[100]  Dieu Tien Bui,et al.  Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..

[101]  Abdul Majid,et al.  Random forest-based scheme using feature and decision levels information for multi-focus image fusion , 2016, Pattern Analysis and Applications.

[102]  H. Pourghasemi GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models , 2016 .

[103]  Robert J. McGaughey,et al.  Mixed severity fire effects within the Rim fire: Relative importance of local climate, fire weather, topography, and forest structure , 2015 .

[104]  F. Pappenberger,et al.  Development of a Global Fire Weather Database , 2015 .

[105]  Hong S. He,et al.  Defining fire environment zones in the boreal forests of northeastern China. , 2015, The Science of the total environment.

[106]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[107]  Witold Pedrycz,et al.  Fuzzy clustering of time series data using dynamic time warping distance , 2015, Eng. Appl. Artif. Intell..

[108]  J. Alday,et al.  Mulch application as post-fire rehabilitation treatment does not affect vegetation recovery in ecosystems dominated by obligate seeders , 2014 .

[109]  O. Sass,et al.  Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests , 2014 .

[110]  Juan de la Riva,et al.  An insight into machine-learning algorithms to model human-caused wildfire occurrence , 2014, Environ. Model. Softw..

[111]  Zhiliang Zhu,et al.  Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China , 2013, Landscape Ecology.

[112]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[113]  Eunchong Kim,et al.  Design and Analysis of Flame Signal Detection with the Combination of UV/IR Sensors , 2013 .

[114]  José M. C. Pereira,et al.  Exploratory spatial data analysis of global MODIS active fire data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[115]  Corinne Lampin,et al.  A Review of the Main Driving Factors of Forest Fire Ignition Over Europe , 2013, Environmental Management.

[116]  Chris Aldrich,et al.  Interpretation of nonlinear relationships between process variables by use of random forests , 2012 .

[117]  J. Greenberg,et al.  Spatial variability in wildfire probability across the western United States , 2012 .

[118]  B. R. Ramesh,et al.  Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India , 2012 .

[119]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[120]  Trisalyn A. Nelson,et al.  Factors influencing national scale wildfire susceptibility in Canada , 2012 .

[121]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[122]  Y. Bergeron,et al.  Impact of Climate Change on Forest Fire Severity and Consequences for Carbon Stocks in Boreal Forest Stands of Quebec, Canada: a Synthesis , 2010 .

[123]  Christoph Schneider,et al.  Spatio‐temporal prediction of snow cover in the Black Forest mountain range using remote sensing and a recurrent neural network , 2010 .

[124]  Thong Ngee Goh,et al.  Adaptive ridge regression system for software cost estimating on multi-collinear datasets , 2010, J. Syst. Softw..

[125]  A. Brenning,et al.  Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques. , 2010 .

[126]  Douglas G. Woolford,et al.  A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain , 2010 .

[127]  Joseph W. Sherlock,et al.  Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA , 2009 .

[128]  Dan Malkinson,et al.  Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape , 2009, European Journal of Forest Research.

[129]  E. Chuvieco,et al.  Human-caused wildfire risk rating for prevention planning in Spain. , 2009, Journal of environmental management.

[130]  Alexandra D. Syphard,et al.  Predicting spatial patterns of fire on a southern California landscape , 2008 .

[131]  Pijush Samui,et al.  Support vector machine applied to settlement of shallow foundations on cohesionless soils , 2008 .

[132]  Shin-Juh Chen,et al.  Fire detection using smoke and gas sensors , 2007 .

[133]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[134]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

[135]  Biswajeet Pradhan,et al.  Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS) , 2007 .

[136]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[137]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[138]  A. R. Mahmud,et al.  GIS‐grid‐based and multi‐criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia , 2004 .

[139]  Andrei B. Utkin,et al.  Development of neural network committee machines for automatic forest fire detection using lidar , 2004, Pattern Recognit..

[140]  Friedrich Recknagel,et al.  Applications of machine learning to ecological modelling , 2001 .

[141]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[142]  Alexandros Dimitrakopoulos,et al.  Flammability Assessment of Mediterranean Forest Fuels , 2001 .

[143]  M. Flannigan,et al.  Climate change and forest fires. , 2000, The Science of the total environment.

[144]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[145]  R. Lunetta,et al.  A change detection experiment using vegetation indices. , 1998 .

[146]  E. Chuvieco,et al.  Application of remote sensing and geographic information systems to forest fire hazard mapping. , 1989 .

[147]  Meng Zhang,et al.  A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors , 2019, IEEE Access.

[148]  Xiao Xiang Zhu,et al.  Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[149]  Prasenjit Chatterjee,et al.  A comparative analysis of VIKOR method and its variants , 2016 .

[150]  K. Solaimani,et al.  Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques , 2012, Natural Hazards.

[151]  D. Weise,et al.  Chapter 2 Climatic and Weather Factors Affecting Fire Occurrence and Behavior , 2008 .

[152]  Anuradha Eaturu,et al.  Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. , 2008, Journal of environmental management.

[153]  E. Pastor,et al.  Mathematical models and calculation systems for the study of wildland fire behaviour , 2003 .

[154]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..