92 citations as recorded by crossref.

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25. A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran O. Mohammed et al. 10.3390/su142013625
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27. 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
28. Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy M. Salis et al. 10.1007/s10661-014-4175-x
29. 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
30. 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
31. 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
32. Wildfire risk estimation in the Mediterranean area A. Ager et al. 10.1002/env.2269
33. Modeling fire ignition patterns in Mediterranean urban interfaces M. Elia et al. 10.1007/s00477-018-1558-5
34. Reassessing wildfire susceptibility and hazard for mainland Portugal S. Oliveira et al. 10.1016/j.scitotenv.2020.143121
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40. Assessing the social context of wildfire-affected areas. The case of mainland Portugal S. Oliveira et al. 10.1016/j.apgeog.2017.09.004
41. 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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45. Negligent and intentional fires in Portugal: Spatial distribution characterization J. Parente et al. 10.1016/j.scitotenv.2017.12.013
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48. Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment L. Dimuccio et al. 10.1071/WF09083
49. Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland–Urban Interfaces in Southern Italy M. Elia et al. 10.1007/s00267-015-0531-z
50. Exploratory Spatial Analysis of Social Vulnerability and Forest Fire Risk in the Pinhal Interior Sul (Central Portugal) J. Farinha et al. 10.3390/su14053010
51. A combined structural and seasonal approach to assess wildfire susceptibility and hazard in summertime R. Bergonse et al. 10.1007/s11069-021-04554-7
52. Promoting Low-Risk Fire Regimes: An Agent-Based Model to Explore Wildfire Mitigation Policy Options P. Ribeiro et al. 10.3390/fire6030102
53. Evaluation of Machine Learning Performance in Wildfire Susceptibility Mapping Under Limited Training Data Condition M. SHAFAPOURTEHRANY 10.46387/bjesr.1174006
54. Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India K. Jodhani et al. 10.1186/s42408-024-00254-2
55. 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
56. The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal M. Pereira et al. 10.3390/fire7070234
57. 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
58. 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
59. Biophysical controls over fire regime properties in Central Portugal R. Bergonse et al. 10.1016/j.scitotenv.2021.152314
60. A Control Algorithm for Early Wildfire Detection Using Aerial Sensor Networks: Modeling and Simulation A. Rocha et al. 10.3390/drones6020044
61. Variable selection for estimating individual tree height using genetic algorithm and random forest E. Nunes Miranda et al. 10.1016/j.foreco.2021.119828
62. 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
63. Bridging knowledge gaps on the role of spatial planning in wildfire risk reduction: insights from Portugal F. Tedim et al. 10.1071/WF22131
64. Assessing Landscape Scale Wildfire Exposure for Highly Valued Resources in a Mediterranean Area F. Alcasena et al. 10.1007/s00267-015-0448-6
65. Assessing wildfire susceptibility and driving variables in Portugal using machine learning approach V. Santana Neto et al. 10.1016/j.jnc.2025.126956
66. A Study on the Susceptibility of Wildfire Disasters in Wuzhou City Based on Interpretability Model L. Zou et al. 10.1109/ACCESS.2025.3583089
67. 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
68. Analyzing spatiotemporal changes in wildfire regime and exposure across a Mediterranean fire-prone area M. Salis et al. 10.1007/s11069-013-0951-0
69. 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
70. 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
71. Development of a Model to Estimate the Risk of Emission of Greenhouse Gases from Forest Fires V. Lerma-Arce et al. 10.3390/fire6010008
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73. Effects of different sampling strategies for unburned label selection in machine learning modelling of wildfire occurrence probability X. Quan et al. 10.1071/WF21149
74. A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing E. Oliveira et al. 10.3390/rs13245164
75. 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
76. 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
77. Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia M. Shafapourtehrany 10.3390/fire6010022
78. Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN) R. Kanwal et al. 10.3390/rs15215099
79. Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping M. Tehrany et al. 10.1155/2021/6638241
80. Forest owners and fuels management coordination. When neighbours’ actions matter M. Canadas & A. Novais 10.1080/02827581.2018.1530369
81. Wildfire hazard mapping in the eastern Mediterranean landscape A. Trucchia et al. 10.1071/WF22138
82. A multivariate approach to assess the structural determinants of large wildfires: evidence from a Mediterranean country D. Sousa et al. 10.1071/WF20119
83. Wildfire Risk Levels at the Local Scale: Assessing the Relative Influence of Hazard, Exposure, and Social Vulnerability R. Bergonse et al. 10.3390/fire5050166
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85. Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea Y. Piao et al. 10.1080/19475705.2022.2030808
86. 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
87. 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
88. Optimizing the location of aerial resources to combat wildfires: a case study of Portugal J. Zeferino 10.1007/s11069-020-03856-6
89. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey H. Akıncı & H. Akıncı 10.1007/s12145-023-00953-5
90. Is Portugal’s forest transition going up in smoke? T. Oliveira et al. 10.1016/j.landusepol.2017.04.046
91. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods W. Chen et al. 10.1016/j.scitotenv.2019.05.312
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