Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset
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S. V. Razavi-Termeh | A. Sadeghi-Niaraki | Soo-Mi Choi | Khalifa M. Al-Kindi | Behrokh Nazeri | Tamer Abuhmed | Nazanin Bahadori
[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..