Citation

Salavati G, Saniei E, Ghaderpour E, Hassan QK. Sustainability (Basel) 2022; 14(7): e3881.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/su14073881

PMID

unavailable

Abstract

The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, which is located in the west of the province of Kurdistan and the west of Iran. In this study, fire risk potential was assessed using weights of evidence (WoE) and statistical index (SI) models. Information about fire incidents in Sanandaj (2011-2020) was divided into two parts: educational data (2011-2017) and validation data (2018-2020). Factors considered for potential forest and rangeland fire risk in Sanandaj city included altitude, slope percentage, slope direction, distance from the road, distance from the river, land use/land cover (LULC), average annual rainfall, and average annual temperature. Finally, in order to validate the two models used, the receiver operating characteristic (ROC) curve was used. The results for the WoE and SI models showed that about 62.96% and 52.75% of the study area, respectively, were in the moderate risk to very high risk classes. In addition, the results of the ROC curve analysis showed that the WoE and SI models had area under the curve (AUC) values of 0.741 and 0.739, respectively. Although the input parameters for both models were the same, the WoE model showed a slightly higher AUC value compared to the SI model, and can potentially be used to predict future fire risk in the study area. The results of this study can help decision makers and managers take the necessary precautions to prevent forest and rangeland fires and/or to minimize fire damage.

Language: en

Keywords

elevation; forecasting; geographic information system; Kurdistan; land use/land cover; Sanandaj county; statistical index; weights of evidence; wildfire risk; Zagros forests