Zolfaghari, Ali Asghar, Raeesi, Maryam, Sheikh, Zahra, Soltani, Azadeh, Poodineh, Soghra, Amiri, Mojtaba (2025). Enhancing Fire Susceptibility Mapping in Semnan Province: Integrating Machine Learning and Geospatial Analysis. , 13(1), 13-36. doi: 10.22034/ECOPERSIA.13.1.13
Ali Asghar Zolfaghari; Maryam Raeesi; Zahra Sheikh; Azadeh Soltani; Soghra Poodineh; Mojtaba Amiri. "Enhancing Fire Susceptibility Mapping in Semnan Province: Integrating Machine Learning and Geospatial Analysis". , 13, 1, 2025, 13-36. doi: 10.22034/ECOPERSIA.13.1.13
Zolfaghari, Ali Asghar, Raeesi, Maryam, Sheikh, Zahra, Soltani, Azadeh, Poodineh, Soghra, Amiri, Mojtaba (2025). 'Enhancing Fire Susceptibility Mapping in Semnan Province: Integrating Machine Learning and Geospatial Analysis', , 13(1), pp. 13-36. doi: 10.22034/ECOPERSIA.13.1.13
Zolfaghari, Ali Asghar, Raeesi, Maryam, Sheikh, Zahra, Soltani, Azadeh, Poodineh, Soghra, Amiri, Mojtaba Enhancing Fire Susceptibility Mapping in Semnan Province: Integrating Machine Learning and Geospatial Analysis. , 2025; 13(1): 13-36. doi: 10.22034/ECOPERSIA.13.1.13

Enhancing Fire Susceptibility Mapping in Semnan Province: Integrating Machine Learning and Geospatial Analysis

ECOPERSIA
Article 2, Volume 13, Issue 1 - Serial Number 51, 2025, Pages 13-36    PDF (4.35 M)
Document Type: Original Research
DOI: 10.22034/ECOPERSIA.13.1.13
Authors
Ali Asghar Zolfaghari* 1; Maryam Raeesi2; Zahra Sheikh2; Azadeh Soltani2; Soghra Poodineh2; Mojtaba Amiri3
1Associate professor, Faculty of Desert Studies, Semnan University, Semnan, Iran
2Faculty of Desert Studies, Semnan University, Semnan, Iran
3Associate professor, Faculty of Natural Resources, Semnan University, Semnan, Iran
Abstract
Aims: This study assesses the impacts of natural and human factors on fire occurrences, identifies key contributors to fire susceptibility maps, and employs machine learning algorithms (MLAs) to enhance the spatiotemporal patterns of fire susceptibility maps.

Materials & Methods: Data were collected from 110 fire locations and 110 non-fire points spanning from 2001 to 2022 at annual scale. Various auxiliary variables, including climate data, terrain features, Normalized Difference Vegetation Index (NDVI), and distance to roads, were analyzed to model fire susceptibility. The study employed multiple MLAs, including Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Decision Trees (GBDT), to generate the fire susceptibility maps.

Findings: About 70% of fires occurred within 2 km of roads, indicating significant human influence. Grasslands had the highest fire rates, with over 25% of fires from 2001-2022 due to flammable fuels. The RF and mean models identified 0.4% and 1.31% of the area as very high susceptibility (38,800 km² and 12,600 km²), while the GBDT and SVM models identified 2.42% and 1.86% (234,700 km² and 180,000 km²). The very high susceptibility class, though small in percentage, covers large areas.

Conclusion: This research highlights the importance of integrating environmental and human factors for predicting fire events in arid regions and developing comprehensive fire susceptibility maps, critical for protecting vulnerable ecosystems. These outcomes provide valuable tools for fire management and mitigation strategies within vulnerable ecosystems. Moreover, developing targeted fire management strategies focused on high-risk areas, such as juniper and broadleaf forests must be a priority.
Keywords
Auxiliary variables; Climatic indicators; Juniper Forest; Distance to Road; Fire Susceptibility
References
Statistics
Article View: 7,452
PDF Download: 10,582
Statistics
Number of Journals45
Number of Issues2,171
Number of Articles24,674
Article View24,449,187
PDF Download17,555,702