Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data
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Bahareh Kalantar | Saeid Janizadeh | Mohammed Oludare Idrees | Naonori Ueda | Kourosh Dadashtabar Ahmadi | Farzin Shabani | N. Ueda | F. Shabani | Saeid Janizadeh | K. Ahmadi | B. Kalantar | M. Idrees
[1] H. Pourghasemi,et al. The temporal and spatial relationships between climatic parameters and fire occurrence in northeastern Iran , 2020 .
[2] Thomas Blaschke,et al. Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models , 2020, Global Ecology and Conservation.
[3] Naonori Ueda,et al. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data , 2020, Remote. Sens..
[4] T. Danaher,et al. A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest , 2020 .
[5] Mahyat Shafapour Tehrany,et al. A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS , 2020, Forecasting.
[6] Mark Crowley,et al. A review of machine learning applications in wildfire science and management , 2020, Environmental Reviews.
[7] R. Lasaponara,et al. Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. , 2020, Environmental research.
[8] H. Pourghasemi,et al. Assessing and mapping multi-hazard risk susceptibility using a machine learning technique , 2020, Scientific Reports.
[9] Chang-Wook Lee,et al. Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA , 2020, Remote. Sens..
[10] A. Hudak,et al. Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality , 2020, Remote Sensing of Environment.
[11] Amir Mosavi,et al. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. , 2019, The Science of the total environment.
[12] Bahareh Kalantar,et al. Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms , 2019, Remote Sensing.
[13] David P. Roy,et al. Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach , 2019, Remote Sensing of Environment.
[14] Thomas Blaschke,et al. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches , 2019, Fire.
[15] Alfian Abdul Halin,et al. Conditioning Factors Determination for Landslide Susceptibility Mapping Using Support Vector Machine Learning , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.
[16] 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.
[17] H. Pourghasemi,et al. Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park , 2019, Forests.
[18] Thomas Blaschke,et al. Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables , 2019, Fire.
[19] 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.
[20] Jan Dirk Wegner,et al. Country-wide high-resolution vegetation height mapping with Sentinel-2 , 2019, Remote Sensing of Environment.
[21] 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.
[22] Dieu Tien Bui,et al. Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics , 2019, Remote. Sens..
[23] E. Chuvieco,et al. Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa , 2019, Remote Sensing of Environment.
[24] Meng Zhang,et al. A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors , 2019, IEEE Access.
[25] K. P. Ferentinos,et al. Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017 , 2018, Applied Geography.
[26] Nhat-Duc Hoang,et al. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method , 2018, Ecol. Informatics.
[27] 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.
[28] H. Meilby,et al. Multi-purpose forest management in the tropics: Incorporating values of carbon, biodiversity and timber in managing Tectona grandis (teak) plantations in Costa Rica , 2018, Forest Ecology and Management.
[29] Gabriel Navarro,et al. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[30] Abolfazl Jaafari,et al. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran , 2017, Ecol. Informatics.
[31] Jennifer K. Balch,et al. Human-started wildfires expand the fire niche across the United States , 2017, Proceedings of the National Academy of Sciences.
[32] 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 .
[33] Zohre Sadat Pourtaghi,et al. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .
[34] 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..
[35] Wang Wenhui,et al. Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest , 2016 .
[36] L. Prasetyo,et al. Mapping of Fire Vulnerability in Alas Purwo National Park , 2016 .
[37] George P. Petropoulos,et al. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada , 2015 .
[38] Zohre Sadat Pourtaghi,et al. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.
[39] Lawrence Ong,et al. Landsat-8 Operational Land Imager Radiometric Calibration and Stability , 2014, Remote. Sens..
[40] Nur Algeet-Abarquero,et al. Soil erosion under teak (Tectona grandis L.f.) plantations: General patterns, assumptions and controversies , 2014 .
[41] Ljiljana Šerić,et al. Intelligent forest fire monitoring system , 2012, Inf. Syst. Frontiers.
[42] Michael W. Binford,et al. A spatio-temporal analysis of fire recurrence and extent for semi-arid savanna ecosystems in Southern Africa using moderate-resolution satellite imagery. , 2012, Journal of environmental management.
[43] Pintu Sen,et al. International Conference on Recent Trends in Physics (ICRTP 2014) , 2012 .
[44] Michael R. Chernick,et al. Resampling Methods , 2002 .
[45] Imad H. Elhajj,et al. Artificial intelligence for forest fire prediction , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.
[46] H. B. Mitchell. Re-sampling Methods , 2010 .
[47] Ewout W. Steyerberg,et al. Overfitting and optimism in prediction models , 2009 .
[48] J. Randerson,et al. The Impact of Boreal Forest Fire on Climate Warming , 2006, Science.
[49] P. Erskine,et al. Restoration of Degraded Tropical Forest Landscapes , 2005, Science.
[50] J. Bruinsma. World Agriculture: Towards 2015/2030: An Fao Perspective , 2002 .
[51] P. Good. Resampling Methods , 1999, Birkhäuser Boston.
[52] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[53] J. Friedman. Multivariate adaptive regression splines , 1990 .
[54] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .