Paper Info
Title
Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods.
Abstract
This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables.
Year
DOI
Venue
2020
10.3390/rs12101689
REMOTE SENSING
Keywords
DocType
Volume
forest fire,artificial intelligence,ubiquitous GIS,ensemble models,mobile application
Journal
12
Issue
Citations 
PageRank 
10
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Seyed Vahid Razavi Termeh100.68
Abolghasem Sadeghi-Niaraki2296.53
Soo-Mi Choi301.35