Anthropogenic activities amplify wildfire occurrence in the Zagros eco-region of western Iran

Abolfazl Jaafari (), Omid Rahmati, Eric K. Zenner and Davood Mafi-Gholami
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Abolfazl Jaafari: Agricultural Research, Education, and Extension Organization (AREEO)
Omid Rahmati: AREEO
Eric K. Zenner: The Pennsylvania State University
Davood Mafi-Gholami: Shahrekord University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 1, No 19, 457-473

Abstract: Abstract The aim of this study was to improve our understanding of factors that affect the spatial distribution of wildfire occurrences at the regional scale. We employed the random forest, boosted regression tree, and genetic algorithm rule-set production models to assess the spatial interplay between fire events and climate, topography, and anthropogenic factors in order to characterize wildfire occurrence in the Zagros eco-region of western Iran. We constructed a geospatial database using the historical fires from the period 2007–2020 and topography, climate, and human related factors. The results demonstrated that human activities (i.e., land use and distance from the settlements and roads) contributed 45% to the probability model of wildfire occurrence in the study region. The models ranked the climate factors (rainfall, temperature, and wind effect) as the second most influential drivers of fire occurrences, whereas topographic features (slope, elevation, and aspect) did not significantly influence fire probability in the landscape. Overall model performance was assessed with the area under the receiver operating characteristic (AUROC) method that showed the superior performance of the RF model in the training phase (AUROC = 0.92) and in its ability to predict upcoming fires (AUROC = 0.90). The insights obtained from this research can bring into focus both the locations and the types of suppression policies that are required to alleviate the effects of the upcoming wildfires in the early twenty-first century.

Keywords: Human activity; Geographic information systems (GIS); Machine learning; Probability mapping (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05397-6

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