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Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches


Ghorbanzadeh, O and Valizadeh Kamran, K and Blaschke, T and Aryal, J and Naboureh, A and Einali, J and Bian, J, Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches, Fire, 2, (3) Article 43. ISSN 2571-6255 (2019) [Refereed Article]


Copyright Statement

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3390/fire2030043


Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

Item Details

Item Type:Refereed Article
Keywords:artificial neural network (ANN), support vector machines (SVM), random forest (RF), k-fold cross-validation (CV), MODIS
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Natural hazards
Objective Field:Natural hazards not elsewhere classified
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:134258
Year Published:2019
Web of Science® Times Cited:58
Deposited By:Geography and Spatial Science
Deposited On:2019-08-05
Last Modified:2020-05-25
Downloads:17 View Download Statistics

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