42 citations as recorded by crossref.

1. Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios J. Choi & H. Chae 10.3390/atmos16010005
2. An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques O. Mofokeng et al. 10.3390/fire7020061
3. Integrated wildfire danger models and factors: A review I. Zacharakis & V. Tsihrintzis 10.1016/j.scitotenv.2023.165704
4. Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side E. Aksoy et al. 10.3390/fire6100408
5. Fire classification in natural ecosystems by physical and environmental characteristics L. Chernogor et al. 10.26565/1992-4259-2023-29-05
6. Modelling Fire Behavior to Assess Community Exposure in Europe: Combining Open Data and Geospatial Analysis P. Palaiologou et al. 10.3390/ijgi11030198
7. Mathematical models for estimate of the ecological consequences of the impact of the pyrogenic factor on forest ecosystems L. Chernogor et al. 10.26565/1992-4259-2022-27-04
8. Study on the Wildfires Occurring Risk Based on Fuzzy FTA C. Chen et al. 10.1088/1755-1315/440/5/052100
9. Seasonal Differences in the Spatial Patterns of Wildfire Susceptibility and Drivers in Southwest Mountains, China W. Wang et al. 10.2139/ssrn.4193547
10. Global and Regional Trends and Drivers of Fire Under Climate Change M. Jones et al. 10.1029/2020RG000726
11. Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe R. Cilli et al. 10.1038/s41598-022-20347-9
12. Uncovering current pyroregions in Italy using wildfire metrics M. Elia et al. 10.1186/s13717-022-00360-6
13. Managing Wildfire Risk in Mosaic Landscapes: A Case Study of the Upper Gata River Catchment in Sierra de Gata, Spain M. Bertomeu et al. 10.3390/land11040465
14. Lightning-caused fires in the Alps: Identifying the igniting strokes J. Moris et al. 10.1016/j.agrformet.2020.107990
15. Recommendations for Ensuring Environmental Safety of Ecosystem Restoration After Fire 10.26565/1992-4259-2020-23-04
16. Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level A. Trucchia et al. 10.3390/fire5010030
17. Determination of forest fire risk with respect to Marchalina hellenica potential distribution to protect pine honey production sites in Turkey F. Sarı et al. 10.1007/s11356-024-34664-1
18. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability A. Jaafari et al. 10.1016/j.jenvman.2019.04.117
19. A review of machine learning applications in wildfire science and management P. Jain et al. 10.1139/er-2020-0019
20. Assessing the Role of Forest Grazing in Reducing Fire Severity: A Mitigation Strategy R. Lovreglio et al. 10.3390/fire7110409
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22. Analysis of Wildfire Fault Based on F-FTA Method C. Chen et al. 10.1088/1755-1315/300/3/032089
23. Natural disturbances risks in European Boreal and Temperate forests and their links to climate change – A review of modelling approaches J. Machado Nunes Romeiro et al. 10.1016/j.foreco.2022.120071
24. A deep learning ensemble model for wildfire susceptibility mapping A. Bjånes et al. 10.1016/j.ecoinf.2021.101397
25. Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China W. Yue et al. 10.3390/f14081616
26. Assessment of the effects of different variable weights on wildfire susceptibility F. Sari 10.1007/s10342-023-01643-z
27. Forest fire risk under climate change in Austria and comparable European regions D. Henner & G. Kirchengast 10.1016/j.tfp.2025.100889
28. Parameters and environmental consequences of catastrophic fires in Ukraine: modeling, quantitative estimates L. Chernogor et al. 10.26565/1992-4224-2024-42-06
29. Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China W. Wang et al. 10.1016/j.scitotenv.2023.161782
30. A Combination of Human Activity and Climate Drives Forest Fire Occurrence in Central Europe: The Case of the Czech Republic R. Berčák et al. 10.3390/fire7040109
31. Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran M. Morovati et al. 10.1371/journal.pone.0312552
32. A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy M. Tonini et al. 10.3390/geosciences10030105
33. Modelling wildfire activity in wildland–urban interface (WUI) areas of Sardinia, Italy C. Scarpa et al. 10.1071/WF24109
34. Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey S. Giannakidou et al. 10.1016/j.iot.2024.101171
35. Identifying the Areas at Risk of Huaico Occurrences in the Department of Lima, Peru G. Santos et al. 10.3390/cli13010011
36. Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China X. Tang et al. 10.1029/2021EF002199
37. Advancements in Wildfire Detection and Prediction: An In-Depth Review R. SALMAN et al. 10.35940/ijitee.B9774.13020124
38. Towards an integrated forest fire danger assessment system for the European Alps M. Müller et al. 10.1016/j.ecoinf.2020.101151
39. Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles M. Rybansky 10.3390/app12083939
40. Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models F. Sari 10.1007/s11676-022-01502-4
41. Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach K. Babu et al. 10.1016/j.foreco.2023.121057
42. Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: An integrated model to analysis of landscape indicators H. Hong et al. 10.1016/j.ecolind.2019.01.056