Preparation map of Forest Fire Risk Using SVM, RF & MLP Algorithms (Case Study: Golestan National Park, Northeastern Iran)

Background and objectives: Spatial prediction of fire risk and preparing the forest fire risk map across the natural areas are among the ways that can be used to prevent and to manage fire. The aim of this research was zonation of forest fire risk in Golestan National Park using non-parametric algorithms, namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). Materials and methods: About 100 occurred fire points were considered for modeling the fire risk. The effective factors on fire occurring including vegetation types, physiographic, climatic, and human factors were identified and their relevant maps were prepared from different sources. To modeling purposes, initially the zone was divided into 1-ha levels of decision-making and modeling and then the pixel values of the effective factors on classes of fire occurring, across the 1-ha levels, were extracted and standardized. Based on non-parametric algorithms, fire risk was modeled with 70 percent of the fire points, as training samples. The prepared forest fire risk map was zoned in terms of four classes of low-risk, medium-risk, high-risk and dangerous. The classification accuracy of the maps, resulted from this modeling, was assessed through the overall classification accuracy given 30 percent of the remained fire points. Results & Conclusion:The results indicated that RF algorithm, with the overall accuracy of 75%, was the best algorithm in predicting the fire risk compare to other ones. Likewise, after matching the fire risk occurring with the results gained from algorithms, it turned out that all algorithms were able to classify the area properly in terms of the fire risk, as more than 80 percent of fire points were placed in the high-risk and dangerous classes. . Results & Conclusion:The results indicated that RF algorithm, with the overall accuracy of 75%, was the best algorithm in predicting the fire risk compare to other ones. Likewise, after matching the fire risk occurring with the results gained from algorithms, it turned out that all algorithms were able to classify the area properly in terms of the fire risk, as more than 80 percent of fire points were placed in the high-risk and dangerous classes. Results & Conclusion:The results indicated that RF algorithm, with the overall accuracy of 75%, was the best algorithm in predicting the fire risk compare to other ones. Likewise, after matching the fire risk occurring with the results gained from algorithms, it turned out that all algorithms were able to classify the area properly in terms of the fire risk, as more than 80 percent of fire points were placed in the high-risk and dangerous classes