Forest fire susceptibility modeling using hybrid approaches
暂无分享,去创建一个
Thomas Blaschke | Thomas Blaschke | Bakhtiar Feizizadeh | Tobia Lakes | Bakhtiar Feizizadeh | Hassan Abedi Gheshlaghi | Tobia Lakes | Sapna Tajbar | T. Blaschke | B. Feizizadeh | T. Lakes | Sapna Tajbar
[1] B. Pradhan,et al. A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam) , 2015 .
[2] Wei Chen,et al. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.
[3] A. Zhu,et al. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method , 2018 .
[4] Jen-Chih Yao,et al. Strong Convergence Theorems by Hybrid Methods for Two Noncommutative Nonlinear Mappings in Banach Spaces , 2020 .
[5] Philippe De Maeyer,et al. Application of the topographic position index to heterogeneous landscapes , 2013 .
[6] 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 .
[7] B. Pradhan,et al. Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping , 2009 .
[8] Zhu Liang-feng,et al. Risk zonation of landslide in China based on information content model , 2004 .
[9] 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.
[10] B. Pradhan,et al. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya , 2014, Arabian Journal of Geosciences.
[11] 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 .
[12] Thomas Blaschke,et al. An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping , 2018, Environmental Earth Sciences.
[13] A. Arabameri,et al. Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran , 2018, Natural Hazards.
[14] Tarunpreet Bhatia,et al. GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping , 2018, Natural Hazards.
[15] Zohre Sadat Pourtaghi,et al. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .
[16] K. Solaimani,et al. Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran , 2017, Environmental Earth Sciences.
[17] J. Iqbal,et al. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China , 2017, Journal of Mountain Science.
[18] A. Al-Abadi,et al. A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region , 2015, Environmental Monitoring and Assessment.
[19] H. Shahabi,et al. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. , 2018, Journal of environmental management.
[20] B. Pradhan,et al. Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models , 2015, Geosciences Journal.
[21] Zohre Sadat Pourtaghi,et al. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.
[22] M. Sakizadeh,et al. Novel hybrid methods applied for spatial prediction of mercury and variable selection of trace elements in coastal areas of USA. , 2020, Marine pollution bulletin.
[23] H. Pourghasemi,et al. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran , 2014, International Journal of Environmental Science and Technology.
[24] Huang,et al. Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods , 2019, Geosciences.
[25] T. Blaschke,et al. Mapping potential nature-based tourism areas by applying GIS-decision making systems in East Azerbaijan Province, Iran , 2019, Journal of Ecotourism.
[26] S. Mukherjee,et al. Forest fire risk zone mapping from satellite imagery and GIS , 2002 .
[27] Biswajeet Pradhan,et al. Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran , 2017, Environmental Earth Sciences.
[28] 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.
[29] Biswajeet Pradhan,et al. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS) , 2007 .
[30] Thomas Blaschke,et al. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches , 2019, Fire.
[31] B. Pradhan,et al. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy , 2016 .
[32] Dieu Tien Bui,et al. An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling , 2020 .
[33] T. Blaschke,et al. GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran , 2012, Natural Hazards.
[34] Thomas Blaschke,et al. Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables , 2019, Fire.
[35] Xu Dong,et al. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China , 2005, Journal of Forestry Research.
[36] 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..
[37] Saro Lee. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping , 2007 .
[38] Nhat-Duc Hoang,et al. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method , 2018, Ecol. Informatics.
[39] Hassan Abedi Gheshlaghi. Using GIS to Develop a Model for Forest Fire Risk Mapping , 2019, Journal of the Indian Society of Remote Sensing.
[40] Bakhtiar Feizizadeh,et al. Fuzzy Analytical Hierarchical Process and Spatially Explicit Uncertainty Analysis Approach for Multiple Forest Fire Risk Mapping , 2015 .
[41] Thomas E. Dilts,et al. A Weights-of-Evidence Model for Mapping the Probability of Fire Occurrence in Lincoln County, Nevada , 2009 .
[42] Bruna E. Z. Leal,et al. Onboard fuzzy logic approach to active fire detection in Brazilian amazon forest , 2016, IEEE Transactions on Aerospace and Electronic Systems.
[43] Bakhtiar Feizizadeh,et al. Vulnerability evaluation of urban buildings to various earthquake intensities: a case study of the municipal zone 9 of Tehran , 2019, Human and Ecological Risk Assessment: An International Journal.
[44] Ching-Hsue Cheng,et al. Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight , 1994, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..
[45] K. Beven,et al. A physically based, variable contributing area model of basin hydrology , 1979 .
[46] Thomas Blaschke,et al. Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS , 2013 .
[47] 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.
[48] M. Soto. The identification and assessment of areas at risk of forest fire using fuzzy methodology , 2012 .
[49] Bakhtiar Feizizadeh,et al. Integrating GIS Based Fuzzy Set Theory in Multicriteria Evaluation Methods for Landslide Susceptibility Mapping , 2013 .
[50] G. Balamurugan,et al. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India , 2016, Natural Hazards.
[51] Laura A. Burkle,et al. The effects of post-wildfire salvage logging on plant reproductive success and pollination in Symphoricarpos albus, a fire-tolerant shrub , 2019, Forest Ecology and Management.
[52] B. Pradhan,et al. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .
[53] Bakhtiar Feizizadeh,et al. An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping , 2017 .
[54] Fei Liu,et al. Hybrid methods combining atmospheric reanalysis data and a parametric typhoon model to hindcast storm surges in Tokyo Bay , 2019, Scientific Reports.
[55] P. Kayastha. Application of fuzzy logic approach for landslide susceptibility mapping in Garuwa sub-basin, East Nepal , 2012, Frontiers of Earth Science.
[56] Thomas Blaschke,et al. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping , 2014, Int. J. Geogr. Inf. Sci..
[57] Thomas Blaschke,et al. A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆ , 2014, Comput. Geosci..
[58] Behzad Shokati,et al. Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods , 2019 .
[59] H. Pourghasemi,et al. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.
[60] 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..
[61] Gamal Attiya,et al. Two Efficient Hybrid Methods for Enhancing Pan-Sharpening of Multi-spectral Images Transmitted from Satellite to Ground Stations , 2019, Journal of the Indian Society of Remote Sensing.
[62] S. Mandal,et al. Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model , 2018, Applied Geomatics.
[63] Seyed Amir Naghibi,et al. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.
[64] Nicolò Colombani,et al. A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role , 2019, Sustainability.
[65] 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.
[66] H. Pourghasemi. GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models , 2016 .