Next Article in Journal
Composite Estimators for Forest Growth Derived from Symmetric, Varying-Length Observation Intervals
Next Article in Special Issue
Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana
Previous Article in Journal
Hydraulic Characteristics of Populus euphratica in an Arid Environment
Previous Article in Special Issue
Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data
Article

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

1
Department of Geography, University of Defence, 11000 Belgrade, Serbia
2
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
3
Military Geographical Institute, 11000 Belgrade, Serbia
4
College of Marine Sciences and Engineering, Nanjing Normal University, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Forests 2019, 10(5), 408; https://doi.org/10.3390/f10050408
Received: 18 April 2019 / Revised: 5 May 2019 / Accepted: 7 May 2019 / Published: 11 May 2019
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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. View Full-Text
Keywords: geographic information system; support vector machine; random forest; ensemble model; hazard mapping geographic information system; support vector machine; random forest; ensemble model; hazard mapping
Show Figures

Figure 1

MDPI and ACS Style

Gigović, L.; Pourghasemi, H.R.; Drobnjak, S.; Bai, S. Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests 2019, 10, 408. https://doi.org/10.3390/f10050408

AMA Style

Gigović L, Pourghasemi HR, Drobnjak S, Bai S. Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests. 2019; 10(5):408. https://doi.org/10.3390/f10050408

Chicago/Turabian Style

Gigović, Ljubomir; Pourghasemi, Hamid R.; Drobnjak, Siniša; Bai, Shibiao. 2019. "Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park" Forests 10, no. 5: 408. https://doi.org/10.3390/f10050408

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop