Comparison of logistic regression and neural network methods in fire susceptibility of forest and rangelands, Mazandaran province

Document Type : Research Paper


1 Islamic Azad University، Mamaghan Branch

2 Environmental Science, Hakim Sabzevari University


Fires in natural areas are one of the factors decreasing forested area of northern Iran. In this study, forest and rangelands susceptibility to fire were analyzed using data-driven methods over Mazandaran Province. Fourteen important environmental and anthropogenic parameters influencing forest and rangelands susceptibility to fire were used to model probability of fire susceptibility. Binary logistic regression and artificial neural network, as two well-known data driven methods was then used to evaluate environmental and anthropogenic performance on landfire and map of forest fire susceptibility estimates were prepared in GIS environment. The area under the successive rate curve (AUSC) showed that ANN method modeled forest fire susceptibility with an accuracy of around 88% and BLR with 85%. 21.6% of the total area of Mazandaran province is located in areas with high and very high susceptibility levels of forest and rangeland fire. Overall, ANN method showed promising results to estimate landfire susceptibility. The forestry and rangelands fire susceptibility map presented in this study can be used as a basic map of the strategic planing in Mazandaran province to reduce probability fire damages.


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