Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping
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Naonori Ueda | Bahareh Kalantar | Haluk Ozener | Mohammad Reza Habibi | Vahideh Saeidi | Mahyat Shafapour Tehrany | Farzin Shabani | Fariborz Shabani | N. Ueda | M. Habibi | F. Shabani | H. Ozener | B. Kalantar | V. Saeidi | F. Shabani | M. S. Tehrany
[1] Peter Troch,et al. Influence of terrain aspect on water partitioning, vegetation structure and vegetation greening in high‐elevation catchments in northern New Mexico , 2016 .
[2] Jung Hyun Lee,et al. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping , 2014 .
[3] 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.
[4] H. Pourghasemi. GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models , 2016 .
[5] D. Dominey-Howes,et al. Validating a Tsunami Vulnerability Assessment Model (the PTVA Model) Using Field Data from the 2004 Indian Ocean Tsunami , 2007 .
[6] Arthur P. Dempster,et al. Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[7] S. Page,et al. In the line of fire: the peatlands of Southeast Asia , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.
[8] Wei Chen,et al. Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree , 2019, Geocarto International.
[9] Geoffrey H. Donovan,et al. Valuing morbidity effects of wildfire smoke exposure from the 2007 Southern California wildfires , 2016 .
[10] J. Koprowski,et al. Differential response to fire by an introduced and an endemic species complicates endangered species conservation , 2016 .
[11] C. Bryant. Understanding bushfire : trends in deliberate vegetation fires in Australia , 2008 .
[12] Mikhail F. Kanevski,et al. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches , 2018, Environ. Model. Softw..
[13] Liba Pejchar,et al. Mitigation for energy development fails to mimic natural disturbance for birds and mammals , 2017 .
[14] Nilton Cesar Fiedler,et al. Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. , 2016, Journal of environmental management.
[15] Biswajeet Pradhan,et al. Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[16] Y. Tesfaigzi,et al. Wildfire smoke exposure and human health: Significant gaps in research for a growing public health issue. , 2017, Environmental toxicology and pharmacology.
[17] Biswajeet Pradhan,et al. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree , 2016, Landslides.
[18] D. Bromwich,et al. Recent precipitation trends, flash floods and landslides in southern Brazil , 2016 .
[19] S. Cumming. FOREST TYPE AND WILDFIRE IN THE ALBERTA BOREAL MIXEDWOOD: WHAT DO FIRES BURN? , 2001 .
[20] W. W. Miller,et al. Wildfire effects on soil nutrients and leaching in a tahoe basin watershed. , 2006, Journal of environmental quality.
[21] N. Nicholls,et al. Interannual variations of area burnt in Tasmanian bushfires: relationships with climate and predictability , 2007 .
[22] D. Mercer,et al. Evaluating Alternative Prescribed Burning Policies to Reduce Net Economic Damages from Wildfire , 2007 .
[23] J. Zêzere,et al. Assessment and validation of wildfire susceptibility and hazard in Portugal , 2009 .
[24] Futao Guo,et al. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests , 2016 .
[25] Biswajeet Pradhan,et al. A New Semiautomated Detection Mapping of Flood Extent From TerraSAR-X Satellite Image Using Rule-Based Classification and Taguchi Optimization Techniques , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[26] Jan Hauke,et al. Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data , 2011 .
[27] J. Miesel,et al. Wildfire effects on soil properties in fire-prone pine ecosystems: Indicators of burn severity legacy over the medium term after fire , 2019, Applied Soil Ecology.
[28] L. Ayalew,et al. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .
[29] J. W. Kean,et al. Estimating post-fire debris-flow hazards prior to wildfire using a statistical analysis of historical distributions of fire severity from remote sensing data , 2018 .
[30] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .
[31] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using integrated bivariate and multivariate statistical models , 2014, Environmental Earth Sciences.
[32] Zohre Sadat Pourtaghi,et al. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.
[33] K. Carlson,et al. Effects of Wildfire on River Water Quality and Riverbed Sediment Phosphorus , 2015, Water, Air, & Soil Pollution.
[34] P. Nyman,et al. Wildfire effects on water quality in forest catchments: A review with implications for water supply , 2011 .
[35] Pauline F. Grierson,et al. Fire severity impacts on tree mortality and post-fire recruitment in tall eucalypt forests of southwest Australia , 2020 .
[36] S. Stephens,et al. The Effects of Forest Fuel-Reduction Treatments in the United States , 2012 .
[37] Abbas Alimohammadi,et al. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping , 2010, Comput. Geosci..
[38] Omid Rahmati,et al. Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models , 2018 .
[39] Gary J. Sheridan,et al. Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel , 2015 .
[40] Philip N. Omi,et al. The use of shaded fuelbreaks in landscape fire management. , 2000 .
[41] Christopher A. Hiemstra,et al. Interactive effects of wildfire and climate on permafrost degradation in Alaskan lowland forests , 2015 .
[42] B. Pradhan,et al. Remote Sensing Data Derived Parameters and its Use in Landslide Susceptibility Assessment Using Shannon’s Entropy and GIS , 2012 .
[43] A. Ozdemir,et al. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .
[44] M. Obrist,et al. Arthropod biodiversity after forest fires: winners and losers in the winter fire regime of the southern Alps , 2004 .
[45] Dirk Van,et al. Ensemble Methods: Foundations and Algorithms , 2012 .
[46] J. Keeley. Fire intensity, fire severity and burn severity: a brief review and suggested usage , 2009 .
[47] B. Pradhan,et al. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models , 2010 .
[48] Biswajeet Pradhan,et al. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of , 2012 .
[49] Mark Crowley,et al. A review of machine learning applications in wildfire science and management , 2020, Environmental Reviews.
[50] J. Dupuy,et al. Slope effect on laboratory fire spread: contribution of radiation and convection to fuel bed preheating , 2011 .
[51] Ariel M. Aloe,et al. Extracting the Variance Inflation Factor and Other Multicollinearity Diagnostics from Typical Regression Results , 2017 .
[52] R. Heinsohn,et al. Loss of habitat for a secondary cavity nesting bird after wildfire , 2016 .
[53] A. Gill,et al. The worldwide "wildfire" problem. , 2013, Ecological applications : a publication of the Ecological Society of America.
[54] George Pallis,et al. Use of unmanned vehicles in search and rescue operations in forest fires: advantages and limitations observed in a field trial , 2015 .
[55] Mario Parise,et al. Wildfire impacts on the processes that generate debris flows in burned watersheds , 2012, Natural Hazards.
[56] Mustafa Neamah Jebur,et al. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .
[57] D. Pamučar,et al. Modeling the Spatial Variability of Forest Fire Susceptibility Using Geographical Information Systems and the Analytical Hierarchy Process , 2019, Spatial Modeling in GIS and R for Earth and Environmental Sciences.
[58] Carlos Brun,et al. Enhancing multi-model forest fire spread prediction by exploiting multi-core parallelism , 2014, The Journal of Supercomputing.
[59] Domingos Xavier Viegas,et al. Slope and wind effects on fire propagation , 2004 .
[60] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[61] Marj Tonini,et al. Space-time clustering analysis of wildfires: The influence of dataset characteristics, fire prevention policy decisions, weather and climate. , 2016, The Science of the total environment.
[62] Yufeng Shi,et al. Landslide Stability Analysis Based on Generalized Information Entropy , 2009, ESIAT.
[63] C. J. West,et al. Vegetation patterns, plant distribution and life forms across the alpine zone in southern Tierra del Fuego, Argentina , 2001 .
[64] M. Yebra,et al. The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildfire predictions , 2019, Remote Sensing of Environment.
[65] M. C. Kennedy,et al. Fuel treatments and landform modify landscape patterns of burn severity in an extreme fire event. , 2014, Ecological applications : a publication of the Ecological Society of America.
[66] E. Steel,et al. Multi-Model Forecasts of Very-Large Fire Occurences during the End of the 21st Century , 2018, Climate.
[67] Matthieu Kervyn,et al. Thermal remote sensing of the low‐intensity carbonatite volcanism of Oldoinyo Lengai, Tanzania , 2008 .
[68] Omid Ghorbanzadeh,et al. Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping , 2020, Symmetry.
[69] N. Ueda,et al. Application of Machine Learning Algorithms and Their Ensemble for Landslide Susceptibility Mapping , 2020, Understanding and Reducing Landslide Disaster Risk.
[70] Hamid Reza Pourghasemi,et al. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping , 2018, Geoderma.
[71] M. Bednarik,et al. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania) , 2011 .
[72] Hamid Reza Pourghasemi,et al. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping , 2016 .
[73] D. Stow,et al. Tracking MODIS NDVI time series to estimate fuel accumulation , 2015 .
[74] Mustafa Neamah Jebur,et al. Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia , 2015, Environmental Earth Sciences.
[75] 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..
[76] Zohre Sadat Pourtaghi,et al. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .
[77] W. Covington,et al. Toward reference conditions: wildfire effects on flora in an old-growth ponderosa pine forest , 2004 .
[78] H. Rostamzadeh,et al. Estimation of flood land use/land cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin , 2019, Geomatics, Natural Hazards and Risk.
[79] Xiaodong Li,et al. Modeling Fire Spread Under Environmental Influence Using A Cellular Automation Approach , 2000 .
[80] Biswajeet Pradhan,et al. Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster–Shafer Theory , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[81] R. Bradstock,et al. NATIONAL FIRE DANGER RATING SYSTEM PROBABALISTIC FRAMEWORK PROJECT , 2014 .
[82] W. Jolly,et al. Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support , 2011 .
[83] Carl N. Skinner,et al. Basic principles of forest fuel reduction treatments , 2005 .
[84] Zhihua Liu,et al. Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. , 2014, The Science of the total environment.
[85] Hamid Reza Pourghasemi,et al. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China , 2018, Comput. Geosci..
[86] Seyed Amir Naghibi,et al. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping , 2017 .
[87] Himan Shahabi,et al. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability , 2019, Agricultural and Forest Meteorology.
[88] Domingos Xavier Viegas,et al. Fire spread in canyons , 2004 .
[89] Bahareh Kalantar,et al. Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data , 2020, Remote. Sens..
[90] 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.
[91] Sassan Saatchi,et al. Estimation of Forest Fuel Load From Radar Remote Sensing , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[92] A. Yalçın. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations , 2008 .
[93] Andrew L. Sullivan,et al. Curvature effects in the dynamic propagation of wildfires , 2016 .
[94] K. S. Reddy,et al. Effect of wind speed and direction on convective heat losses from solar parabolic dish modified cavity receiver , 2016 .
[95] S. Limin,et al. Effects of agricultural land-use change and forest fire on N2O emission from tropical peatlands, Central Kalimantan, Indonesia , 2006 .
[96] Javier Herrero,et al. Topographic effects on solar radiation distribution in mountainous watersheds and their influence on reference evapotranspiration estimates at watershed scale. , 2010 .
[97] Zhi-Hua Zhou,et al. Ensemble Methods: Foundations and Algorithms , 2012 .
[98] Helmi Zulhaidi Mohd Shafri,et al. Maximizing Urban Features Extraction from Multi-sensor Data with Dempster-Shafer Theory and HSI Data Fusion Techniques , 2015 .
[99] 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.
[100] Klaus-Dieter Thoben,et al. Machine learning in manufacturing: advantages, challenges, and applications , 2016 .
[101] Alfian Abdul Halin,et al. A COMPARISON BETWEEN THREE CONDITIONING FACTORS DATASET FOR LANDSLIDE PREDICTION IN THE SAJADROOD CATCHMENT OF IRAN , 2020 .
[102] L. Ayalew,et al. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .
[103] J. Sharples. An overview of mountain meteorological effects relevant to fire behaviour and bushfire risk. , 2009 .
[104] Biswajeet Pradhan,et al. Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks , 2021, Geoscience Frontiers.
[105] K. Itten,et al. LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management , 2004 .
[106] J. Vega,et al. Spatially modeling wildland fire severity in pine forests of Galicia, Spain , 2017, European Journal of Forest Research.
[107] Rina Grant-Biggs. Flammable Australia: Fire Regimes, Biodiversity and Ecosystems in a Changing World , 2012 .
[108] Saro Lee,et al. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data , 2005 .
[109] Grant J. Williamson,et al. Geographic Patterns of Fire Severity Following an Extreme Eucalyptus Forest Fire in Southern Australia: 2013 Forcett-Dunalley Fire , 2018, Fire.
[110] Mário G. Pereira,et al. Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps , 2017, Natural Hazards.
[111] M. Flannigan,et al. Fuel moisture sensitivity to temperature and precipitation: climate change implications , 2015, Climatic Change.
[112] H. Pourghasemi,et al. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances , 2013, Natural Hazards.
[113] Yu Chang,et al. Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China , 2012 .
[114] Simon D. Jones,et al. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques , 2019, CATENA.
[115] Aaron C. Greenville,et al. Impact of 2019–2020 mega-fires on Australian fauna habitat , 2020, Nature Ecology & Evolution.
[116] Binh Thai Pham,et al. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers , 2018, Ecol. Informatics.
[117] R. O’Brien,et al. A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .
[118] H. Benjelloun,et al. A Simple Technique to Estimate the Flammability Index of Moroccan Forest Fuels , 2011 .
[119] Ahmed E. M. Al-Juaidi,et al. Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors , 2018, Arabian Journal of Geosciences.
[120] Giorgos Mallinis,et al. A data-driven approach to assess large fire size generation in Greece , 2017, Natural Hazards.
[121] Lluís Brotons,et al. Predictive modelling of fire occurrences from different fire spread patterns in Mediterranean landscapes , 2015 .
[122] Albert Alvarez,et al. Extreme Fire Severity Patterns in Topographic, Convective and Wind-Driven Historical Wildfires of Mediterranean Pine Forests , 2014, PloS one.