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:: Volume 3, Issue 4 (3-2016) ::
jgit 2016, 3(4): 97-120 Back to browse issues page
Determining Effective Factors on Forest Fire Using the Compound of Geographically Weighted Regression and Genetic Algorithm, a Case Study: Golestan, Iran
Amin Raei, Parham Pahlavani *, Mahdi Hasanlou
University of Tehran
Abstract:   (3714 Views)

Determining the effective factors on fire is so important, because the plenty areas of forests around the world are destroyed every year by fire. It helps us to identify most dangerous locations and times in forest fire. Hence, we can prevent many of driving factors of forest fire by law enforcement, efficient forest management policies and more supervision. In the current study, we identified the impressive factors on the fire in Golestan forest using the compound of Geographically Weighted Regression (GWR) method and Genetic Algorithm that is suitable for the spatial regression problem, because it obtains the effective factors considering the autocorrelation and non-stationarity properties of spatial data. In this study, three different fire areas as well as two kernels of Gaussian and Tricube for weighting of GWR were used that for these three fire areas resulted to R2=0.9538, R2=0.9990, and R2=0.9903 for Gaussian kernel and R2=0.9931, R2=0.9999, and R2=0.9980 for Tricube kernel, respectively. This research shows that both of the biophysical and anthropogenic factors have significant effects on forest fire in our study areas. In biophysical factors, the elevation, the aspect, the minimum and mean tempreture and in anthropogenic factors, the landuse and the distance from the residential areas were identified as the most impressive factors. Weighting by Tricube kernel concluded to more precise results.

Keywords: Forest Fire, Geographically Weighted Regression, Genetic Algorithm, Golestan Forest
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Type of Study: Research |
Received: 2016/07/3 | Accepted: 2016/07/3 | Published: 2016/07/3
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Raei A, Pahlavani P, Hasanlou M. Determining Effective Factors on Forest Fire Using the Compound of Geographically Weighted Regression and Genetic Algorithm, a Case Study: Golestan, Iran . jgit. 2016; 3 (4) :97-120
URL: http://jgit.kntu.ac.ir/article-1-308-en.html


Volume 3, Issue 4 (3-2016) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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