Iranian Journal of Forest

Iranian Journal of Forest

Spatial Modeling of Wildfire Susceptibility Based on the Impact of Forest Roads Using Machine Learning Methods in the Western Forests of Mazandaran Province

Document Type : Research Paper

Authors
1 Prof., Forest Engineering, Forestry and Forest Economics Dept. Faculty of Natural Resources, University college of Agriculture and Natural Resources, University of Tehran
2 Ph.D. graduated of Forest Engineering, Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University college of Agriculture and Natural Resources, University of Tehran
3 Prof., Dept. of Soil Science, College of Agriculture, Shiraz University
Abstract
Introduction: The Hyrcanian forests are considered valuable biological assets due to their biodiversity  and ecosystem functions. Wildfires in natural resource areas are among the crises that cause irreparable damage to ecosystems. Therefore, the purpose of this study is to investigate and model the spatial relationship of the forest road network and wildfire susceptibility using modern machine learning methods in the cities of Tankabon and Ramsar.
Materials and methods: The factors affecting wildfire occurrence including land use, distance from the road, height above sea level, soil, typology, road density, climate and slope direction, including existing roads, were investigated and their sensitivity maps were obtained using the Random Forest machine learning method and maximum entropy statistical method. In order to evaluate the models used by receiver operating characteristic curves and the area under the curves and by the response curves and the jackknife test, the percentage of importance of each parameter in the occurrence of wildfire occurrence and its influence in  modeling were determined.
Results: According to Maximum Entropy method, the percentage of importance and participation of the effective factors in the wildfire potential show that the most influencing variables on the fire susceptibility model are height above sea level ( 76%), distance from the road (9.1%), land use (5.4%).  Also, based on the Random Forest model, the parameter of height above sea level with an average of 0.10%, distance from the road 0.9%, land use and vegetation 0.4% have the highest effect. The response curve for  distance from the road and  road density  indicate an increased occurrence of  wildfire  near roads, and roads play a significant role in the occurrence of fire in the region.
Conclusion: The wildfire spatial susceptibility maps show that road distance is an important factor that greatly influences wildfire potential; that is, it can both increase the likelihood of fire occurrence and, conversely, affect fire suppression.
Keywords

Subjects


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Volume 17, Issue 1 - Serial Number 1
Summer 2025
Pages 125-143

  • Receive Date 05 March 2024
  • Revise Date 03 December 2024
  • Accept Date 07 January 2025