91 citations as recorded by crossref.

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25. A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS K. Tshering et al. 10.3390/forecast2020003
26. Local fractality: The case of forest fires in Portugal M. Kanevski & M. Pereira 10.1016/j.physa.2017.02.086
27. Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model A. Abdollahi & B. Pradhan 10.1016/j.scitotenv.2023.163004
28. Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii S. Ramayanti et al. 10.1080/15481603.2024.2353982
29. Assessing the biophysical and social drivers of burned area distribution at the local scale S. Oliveira & J. Zêzere 10.1016/j.jenvman.2020.110449
30. 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) D. Tien Bui et al. 10.1016/j.jenvman.2019.01.108
31. The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal M. Pereira et al. 10.3390/fire7070234
32. Assessment of Forest Fire Risk in Mazandaran Province Using Fuzzy AHP Model S. Biglari-Gholdare et al. 10.61186/ifej.12.2.88
33. Assessment of the recurrence interval of wildfires in mainland Portugal and the identification of affected LUC patterns B. Meneses et al. 10.1080/17445647.2018.1454351
34. A Choice-Modeling Approach to Inform Policies Aimed at Reducing Wildfire Hazard through the Promotion of Fuel Management by Forest Owners J. Santos et al. 10.3390/f12040403
35. Biophysical controls over fire regime properties in Central Portugal R. Bergonse et al. 10.1016/j.scitotenv.2021.152314
36. A Control Algorithm for Early Wildfire Detection Using Aerial Sensor Networks: Modeling and Simulation A. Rocha et al. 10.3390/drones6020044
37. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches M. Leuenberger et al. 10.1016/j.envsoft.2017.12.019
38. Variable selection for estimating individual tree height using genetic algorithm and random forest E. Nunes Miranda et al. 10.1016/j.foreco.2021.119828
39. An efficient, multi-scale neighbourhood index to quantify wildfire likelihood D. Radford et al. 10.1071/WF23055
40. Open Data and Machine Learning to Model the Occurrence of Fire in the Ecoregion of “Llanos Colombo–Venezolanos” J. Barreto & D. Armenteras 10.3390/rs12233921
41. A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa A. Padrão et al. 10.3390/land11071093
42. Bridging knowledge gaps on the role of spatial planning in wildfire risk reduction: insights from Portugal F. Tedim et al. 10.1071/WF22131
43. Assessing Landscape Scale Wildfire Exposure for Highly Valued Resources in a Mediterranean Area F. Alcasena et al. 10.1007/s00267-015-0448-6
44. Assessment of forest fire vulnerability zones in Missouri, United States of America O. Akinola & J. Adegoke 10.1080/13504509.2018.1551815
45. Assessing wildfire susceptibility and driving variables in Portugal using machine learning approach V. Santana Neto et al. 10.1016/j.jnc.2025.126956
46. Assessing exposure of human and ecological values to wildfire in Sardinia, Italy M. Salis et al. 10.1071/WF11060
47. Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression L. Ríos-Pena et al. 10.1016/j.envsoft.2018.03.008
48. Predicting burnt areas during the summer season in Portugal by combining wildfire susceptibility and spring meteorological conditions R. Bergonse et al. 10.1080/19475705.2021.1909664
49. A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran O. Mohammed et al. 10.3390/su142013625
50. Analyzing spatiotemporal changes in wildfire regime and exposure across a Mediterranean fire-prone area M. Salis et al. 10.1007/s11069-013-0951-0
51. The REMAINS R-package: Paving the way for fire-landscape modeling and management S. Pais et al. 10.1016/j.envsoft.2023.105801
52. A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions S. Tavakkoli Piralilou et al. 10.3390/rs14030672
53. Spatial distribution of pyrogenic carbon in Iberian topsoils estimated by chemometric analysis of infrared spectra M. Jiménez-González et al. 10.1016/j.scitotenv.2021.148170
54. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability A. Jaafari et al. 10.1016/j.agrformet.2018.12.015
55. Development of a Model to Estimate the Risk of Emission of Greenhouse Gases from Forest Fires V. Lerma-Arce et al. 10.3390/fire6010008
56. Structural fire risk: The case of Portugal J. Parente & M. Pereira 10.1016/j.scitotenv.2016.08.164
57. Effects of different sampling strategies for unburned label selection in machine learning modelling of wildfire occurrence probability X. Quan et al. 10.1071/WF21149
58. Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy M. Salis et al. 10.1007/s10661-014-4175-x
59. Promoting Landscape-Level Forest Management in Fire-Prone Areas: Delegate Management to a Multi-Owner Collaborative, Rent the Land, or Just Sell It? A. Martins et al. 10.3390/f13010022
60. A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing E. Oliveira et al. 10.3390/rs13245164
61. Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms Y. Piao et al. 10.1080/19475705.2022.2128440
62. A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role G. Busico et al. 10.3390/su11247166
63. Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia M. Shafapourtehrany 10.3390/fire6010022
64. Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN) R. Kanwal et al. 10.3390/rs15215099
65. Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping M. Tehrany et al. 10.1155/2021/6638241
66. Forest owners and fuels management coordination. When neighbours’ actions matter M. Canadas & A. Novais 10.1080/02827581.2018.1530369
67. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data M. Tehrany et al. 10.1007/s00704-018-2628-9
68. 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
69. Wildfire hazard mapping in the eastern Mediterranean landscape A. Trucchia et al. 10.1071/WF22138
70. Wildfire risk estimation in the Mediterranean area A. Ager et al. 10.1002/env.2269
71. Modeling fire ignition patterns in Mediterranean urban interfaces M. Elia et al. 10.1007/s00477-018-1558-5
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76. Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea Y. Piao et al. 10.1080/19475705.2022.2030808
77. Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan W. Rafaqat et al. 10.3390/rs14081918
78. Wildfire aerial thermal image segmentation using unsupervised methods: a multilayer level set approach T. Garcia et al. 10.1071/WF22136
79. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China H. Hong et al. 10.1016/j.scitotenv.2018.02.278
80. Optimizing the location of aerial resources to combat wildfires: a case study of Portugal J. Zeferino 10.1007/s11069-020-03856-6
81. Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series Y. Michael et al. 10.1016/j.scitotenv.2020.142844
82. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey H. Akıncı & H. Akıncı 10.1007/s12145-023-00953-5
83. Is Portugal’s forest transition going up in smoke? T. Oliveira et al. 10.1016/j.landusepol.2017.04.046
84. A new wildland fire danger index for a Mediterranean region and some validation aspects J. de Vicente & F. Crespo 10.1071/WF11046
85. Burned area prediction with semiparametric models M. Boubeta et al. 10.1071/WF15125
86. Integrated wildfire danger models and factors: A review I. Zacharakis & V. Tsihrintzis 10.1016/j.scitotenv.2023.165704
87. Assessing the social context of wildfire-affected areas. The case of mainland Portugal S. Oliveira et al. 10.1016/j.apgeog.2017.09.004
88. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods W. Chen et al. 10.1016/j.scitotenv.2019.05.312
89. Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data Y. Shao et al. 10.1007/s11676-022-01559-1
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