Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques |
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| Institution: | 1. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran;2. Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shaherkord University, Shaherkord, Iran;3. Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA;1. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;2. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;3. Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Viet Nam;4. Civil Engineering, Institute of Research and Development, Duy Tan University. P809 - 03 Quang Trung, Da Nang 550000, Viet Nam;1. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran;2. Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA;3. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, HaNoi, Viet Nam |
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| Abstract: | Forests are living dynamic systems and these unique ecosystems are essential for life on earth. Forest fires are one of the major environmental concerns, economic, and social in the worldwide. The aim of current research is to identify general indicators influencing on forest fire and compare forest fire susceptibility maps based on the boosted regression tree (BRT), generalized additive model (GAM), and random forest (RF) data mining models in the Minudasht Township, Golestan Province, Iran. According to expert opinion and literature review, fifteen condition factors on forest fire have been selected in the study area. These are slope degree, slope aspect, elevation, topographic wetness index (TWI), topographic position index (TPI), plan curvature, wind effect, annual temperature and rainfall, soil texture, distance to roads, rivers, and villages, normalized difference vegetation index (NDVI), and land use. Forest fire locations were identified using MODIS images, historical records, and extensive field checking. 106 (≈70%) locations, out of 151 forest fires identified, were used for models building/training, while the remaining 45 (≈30%) cases were used for the models validation.BRT, GAM, and RF data mining models were used to distinguish between presence and absence of forest fires and its mapping. These algorithms were used to perform feature selection in order to reveal the variables that contribute more to forest fire occurrence. Finally, for validation of models, the area under the curve (AUC) for forest fire susceptibility maps was calculated. The validation of results showed that AUC for three mentioned models varies from 0.7279 to 0.8770 (AUCBRT = 80.84%, AUCGAM = 87.70%, and AUCRF = 72.79%,). Results indicated that the main drivers of forest fire occurrence were annual rainfall, distance to roads, and land use factors. The results can be applied to primary warning, fire suppression resource planning, and allocation work. |
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| Keywords: | Forest fire susceptibility mapping Boosted regression tree Generalized additive model Random forest GIS Iran |
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