Abstract
The main objectives of this paper are to demonstrate the results of an ensemble learning method based on prediction results of support vector machine and random forest methods using Bayesian average. In this study, we generated susceptibility maps of forest fire using supervised machine learning method (support vector machine—SVM) and its comparison with a versatile machine learning algorithm (random forest—RF) and their ensembles. In order to achieve this, first of all, a forest fire inventory map was constructed using Serbian historical forest fire database, Moderate Resolution Imaging Spectro radiometer (MODIS), Landsat 8 OLI and Worldview-2 satellite images, field surveys, and interpretation of aerial photo images. A total of 126 forest fire locations were identified and randomly divided by a random selection algorithm into two groups, including training (70%) and validation data sets (30%). Forest fire susceptibility maps were prepared using SVM, RF, and their ensemble models using the training dataset and 14 selected different conditioning factors. Finally, to explore the performance of the mentioned models we used the values for area under the curve (AUC) of receiver operating characteristics (ROC). The results depicted that the ensemble model had an AUC = 0.848, followed by the SVM model (AUC = 0.844), and RF model (AUC = 0.834). According to achieved AUC results, it can be deduced that SVM, RF, and their ensemble method had satisfactory performance. The study was applied in the Tara National Park (West Serbia), a region of about 191.7 sq. km distinguished by a very high forest density and a large number of forest fires.
Highlights
Forest fires represent the uncontrolled movements of fire along the forest surface and they are one of the most damaging natural disasters and forces [1]
To explore the performance of the mentioned models we used the values for area under the curve (AUC) of receiver operating characteristics (ROC)
The results depicted that the ensemble model had an AUC = 0.848, followed by the support vector machine (SVM) model (AUC = 0.844), and random forest forest (RF) model (AUC = 0.834)
Summary
Forest fires ( called wildfires) represent the uncontrolled movements of fire along the forest surface and they are one of the most damaging natural disasters and forces [1]. According to Chuvieco [2] and Zheng [3], forest fires have become increasingly widespread, partly due to global warming; since summer periods have become hotter and drier than before, winds are getting stronger and the stability of the rainy periods is disturbed, but above all the changes are a result of human negligence and sometimes ulterior motives. A forest fire turns out to be one of the most critical natural hazards in recent years, and results in a serious loss of human life and terrific damage to the ecological environment and human infrastructure [4]. Desertification and deforestation are ones of the most important effects of wildfires [5]
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