Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors
This study investigates the multifaceted factors influencing wildfire risk in Iran, focusing on the interplay between climatic conditions and human activities. Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Trees, Forests and People |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666719325000020 |
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| author | Ehsan Masoudian Ali Mirzaei Hossein Bagheri |
| author_facet | Ehsan Masoudian Ali Mirzaei Hossein Bagheri |
| author_sort | Ehsan Masoudian |
| collection | DOAJ |
| description | This study investigates the multifaceted factors influencing wildfire risk in Iran, focusing on the interplay between climatic conditions and human activities. Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. Multiple scenarios were developed for this purpose based on the data sampling strategy. The findings revealed that climatic elements such as soil moisture, temperature, and humidity significantly contribute to wildfire susceptibility, while human activities—particularly population density and proximity to powerlines—also played a crucial role. Furthermore, the seasonal impact of each parameter was separately assessed during warm and cold seasons. The results indicated that human-related factors, rather than climatic variables, had a more prominent influence during the seasonal analyses. This research provided new insights into wildfire dynamics in Iran by generating high-resolution wildfire susceptibility maps using advanced machine learning classifiers. The generated maps identified high-risk areas, particularly in the central Zagros region, the northeastern Hyrcanian Forest, and the northern Arasbaran forest, highlighting the urgent need for effective fire management strategies. |
| format | Article |
| id | doaj-art-280ec83d6314427fac4425f4bdae2857 |
| institution | OA Journals |
| issn | 2666-7193 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Trees, Forests and People |
| spelling | doaj-art-280ec83d6314427fac4425f4bdae28572025-08-20T02:00:33ZengElsevierTrees, Forests and People2666-71932025-03-011910077410.1016/j.tfp.2025.100774Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factorsEhsan Masoudian0Ali Mirzaei1Hossein Bagheri2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranFaculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, IranFaculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran; Corresponding author.This study investigates the multifaceted factors influencing wildfire risk in Iran, focusing on the interplay between climatic conditions and human activities. Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. Multiple scenarios were developed for this purpose based on the data sampling strategy. The findings revealed that climatic elements such as soil moisture, temperature, and humidity significantly contribute to wildfire susceptibility, while human activities—particularly population density and proximity to powerlines—also played a crucial role. Furthermore, the seasonal impact of each parameter was separately assessed during warm and cold seasons. The results indicated that human-related factors, rather than climatic variables, had a more prominent influence during the seasonal analyses. This research provided new insights into wildfire dynamics in Iran by generating high-resolution wildfire susceptibility maps using advanced machine learning classifiers. The generated maps identified high-risk areas, particularly in the central Zagros region, the northeastern Hyrcanian Forest, and the northern Arasbaran forest, highlighting the urgent need for effective fire management strategies.http://www.sciencedirect.com/science/article/pii/S2666719325000020WildfireMachine learningClimateHuman activityRemote sensingCloud computing |
| spellingShingle | Ehsan Masoudian Ali Mirzaei Hossein Bagheri Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors Trees, Forests and People Wildfire Machine learning Climate Human activity Remote sensing Cloud computing |
| title | Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| title_full | Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| title_fullStr | Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| title_full_unstemmed | Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| title_short | Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| title_sort | assessing wildfire susceptibility in iran leveraging machine learning for geospatial analysis of climatic and anthropogenic factors |
| topic | Wildfire Machine learning Climate Human activity Remote sensing Cloud computing |
| url | http://www.sciencedirect.com/science/article/pii/S2666719325000020 |
| work_keys_str_mv | AT ehsanmasoudian assessingwildfiresusceptibilityiniranleveragingmachinelearningforgeospatialanalysisofclimaticandanthropogenicfactors AT alimirzaei assessingwildfiresusceptibilityiniranleveragingmachinelearningforgeospatialanalysisofclimaticandanthropogenicfactors AT hosseinbagheri assessingwildfiresusceptibilityiniranleveragingmachinelearningforgeospatialanalysisofclimaticandanthropogenicfactors |