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A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

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  • Osama Ashraf Mohammed

    (Department of Social Sciences, College of Basic Education, University of Halabja, Kurdistan Region, Halabja 46006, Iraq)

  • Sasan Vafaei

    (Government Employee of Natural Resources and Watershed Organization of Iran, Tehran 1955756113, Iran)

  • Mehdi Mirzaei Kurdalivand

    (Environmental Sciences-Environment Assessment, Islamic Azad University of Hamedan, Hamedan 413565174, Iran)

  • Sabri Rasooli

    (Department of Forestry, Faculty of Natural Resources, University of Guilan, Someh Sara 419961377, Iran)

  • Chaolong Yao

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Tongxin Hu

    (Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China)

Abstract

Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

Suggested Citation

  • Osama Ashraf Mohammed & Sasan Vafaei & Mehdi Mirzaei Kurdalivand & Sabri Rasooli & Chaolong Yao & Tongxin Hu, 2022. "A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13625-:d:948987
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    References listed on IDEAS

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    1. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1399-1418, July.
    2. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    4. Hamed Adab & Kasturi Kanniah & Karim Solaimani, 2013. "Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1723-1743, February.
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