Modeling and prediction of forest fire susceptibility areas based on machine learning methods in semi-arid oak forest
DOI:
https://doi.org/10.5281/zenodo.17381856Keywords:
Forest fire modeling, Random Forest, Support Vector Machine, Semi-arid oak forestAbstract
Fire is the most pervasive factor that destroys forest ecosystems, which has negative ecological, economic, and social consequences. Therefore, examining the changes in the situation of the region in terms of fire occurrence danger and also the parameters affecting it will be very useful in the field of fire risk management and control approaches. This is especially true in semi-arid oak forests. The current research process has been completed in several sections; First, 13 parameters including topographical factors (height, slope direction, slope), climatic factors (rainfall, temperature, wind direction, wind speed), biological factors (vegetation cover (NDVI) and soil surface moisture ((normal difference moisture indices (NDWI) and soil surface moisture index (LSWI)) and human-made factors (distance from residential areas, distance from the road, distance from agricultural lands, distance from the forest) as effective factors in forest fire risk were assessed and modulated based on three machine learning methods including GLM, RF and SVM. The raster-based maps related to the desired criteria have been prepared using integrated geographic information systems and remote sensing. In the following, using the data related to the fire occurrence, the forest fire risk map was determined. The model’s accuracy was evaluated using AUC in the ROC curve which indicated RF (AUC = 0.994) as the best model in fire detection compared with GLM and SVM models (AUC_GLM = 0.983; AUC_SVM = 0.971). Based on the results of the RF model, 33.95 and 18.84% of the studied areas were categorized in the low and high fire risk classes. The investigation of the factors affecting the occurrence of fire showed that human-made factors (distance from residential areas, distance from agricultural lands), climatic factors (temperature, wind speed, relative humidity), and topographical factors (elevation) played a more important role in places with a history of fire. Therefore, to reduce the number of fires and damages caused by them. It is necessary to pay attention to the reasons and motivations of the factors that cause fire, to reduce and prevent the opportunity of fire as much as possible.
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