Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS
出版年份 2017 全文链接
标题
Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS
作者
关键词
Natural hazard, Forest fire, Fire ignition, Probability mapping
出版物
International Journal of Environmental Science and Technology
Volume 15, Issue 2, Pages 373-384
出版商
Springer Nature
发表日期
2017-06-07
DOI
10.1007/s13762-017-1371-6
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques
- (2016) Zohre Sadat Pourtaghi et al. ECOLOGICAL INDICATORS
- Towards an establishment of a wildfire risk system in a Mediterranean country
- (2016) Mario Mhawej et al. Ecological Informatics
- Assessment of landslide susceptibility using GIS-based evidential belief function in Patu Khola watershed, Dang, Nepal
- (2016) Amar Deep Regmi et al. Environmental Earth Sciences
- The spatially varying influence of humans on fire probability in North America
- (2016) Marc-André Parisien et al. Environmental Research Letters
- What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests
- (2016) Futao Guo et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran
- (2016) Omid Rahmati et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Exploring spatial patterns and drivers of forest fires in Portugal (1980–2014)
- (2016) A.N. Nunes et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Spatial-statistical analysis of factors determining forest fires: a case study from Golestan, Northeast Iran
- (2016) Omid Abdi et al. Geomatics Natural Hazards & Risk
- Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression
- (2016) Dieu Tien Bui et al. Remote Sensing
- GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran
- (2015) Seyed Amir Naghibi et al. ENVIRONMENTAL MONITORING AND ASSESSMENT
- Fire danger assessment in Iran based on geospatial information
- (2015) Saeedeh Eskandari et al. International Journal of Applied Earth Observation and Geoinformation
- Control of style-of-faulting on spatial pattern of earthquake-triggered landslides
- (2015) T. Gorum et al. International Journal of Environmental Science and Technology
- Modelling static fire hazard in a semi-arid region using frequency analysis
- (2015) Hamed Adab et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models
- (2015) Hamid Reza Pourghasemi SCANDINAVIAN JOURNAL OF FOREST RESEARCH
- Modeling Forest Lightning Fire Occurrence in the Daxinganling Mountains of Northeastern China with MAXENT
- (2015) Feng Chen et al. Forests
- Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem
- (2015) Onur Satir et al. Geomatics Natural Hazards & Risk
- Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran
- (2014) Zohre Sadat Pourtaghi et al. Environmental Earth Sciences
- An insight into machine-learning algorithms to model human-caused wildfire occurrence
- (2014) Marcos Rodrigues et al. ENVIRONMENTAL MODELLING & SOFTWARE
- GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran
- (2014) A. Jaafari et al. International Journal of Environmental Science and Technology
- 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
- (2014) Hamid reza Pourghasemi et al. Geomatics Natural Hazards & Risk
- A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping
- (2013) Omar F. Althuwaynee et al. CATENA
- Managing Forests and Fire in Changing Climates
- (2013) S. L. Stephens et al. SCIENCE
- Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest
- (2012) Sandra Oliveira et al. FOREST ECOLOGY AND MANAGEMENT
- Spatial variability in wildfire probability across the western United States
- (2012) Marc-André Parisien et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques
- (2012) Hamed Adab et al. NATURAL HAZARDS
- The human dimension of fire regimes on Earth
- (2011) David M. J. S. Bowman et al. JOURNAL OF BIOGEOGRAPHY
- Modeling and mapping wildfire ignition risk in Portugal
- (2009) Filipe X. Catry et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain
- (2008) E.J.M. Carranza et al. International Journal of Applied Earth Observation and Geoinformation
- Predicting spatial patterns of fire on a southern California landscape
- (2008) Alexandra D. Syphard et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- Human-caused wildfire risk rating for prevention planning in Spain
- (2008) Jesús Martínez et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region
- (2007) F. Javier Lozano et al. REMOTE SENSING OF ENVIRONMENT
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started