DETERMINING EFFECTIVE FACTORS ON FOREST FIRE USING THE COMPOUND OF GEOGRAPHICALLY WEIGHTED REGRESSION AND GENETIC ALGORITHM, A CASE STUDY: GOLESTAN, IRAN

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RAEI AMIN; PAHLAVANI PARHAM; HASANLOU MAHDI

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Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2016 | Volume:3 | Issue:4 Page(s): 97-120

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Title

DETERMINING EFFECTIVE FACTORS ON FOREST FIRE USING THE COMPOUND OF GEOGRAPHICALLY WEIGHTED REGRESSION AND GENETIC ALGORITHM, A CASE STUDY: GOLESTAN, IRAN

Author(s)

RAEI AMIN | PAHLAVANI PARHAM | HASANLOU MAHDI | Issue Writer Certificate

Keywords

FOREST FIREQ1

GEOGRAPHICALLY WEIGHTED REGRESSIONQ2

GENETIC ALGORITHMQ2

GOLESTAN FORESTQ2

Abstract

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.

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APA: Copy

RAEI, AMIN, PAHLAVANI, PARHAM, & HASANLOU, MAHDI. (2016). DETERMINING EFFECTIVE FACTORS ON FOREST FIRE USING THE COMPOUND OF GEOGRAPHICALLY WEIGHTED REGRESSION AND GENETIC ALGORITHM, A CASE STUDY: GOLESTAN, IRAN. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 3(4), 97-120. SID. https://sid.ir/paper/230056/en

Vancouver: Copy

RAEI AMIN, PAHLAVANI PARHAM, HASANLOU MAHDI. DETERMINING EFFECTIVE FACTORS ON FOREST FIRE USING THE COMPOUND OF GEOGRAPHICALLY WEIGHTED REGRESSION AND GENETIC ALGORITHM, A CASE STUDY: GOLESTAN, IRAN. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2016;3(4):97-120. Available from: https://sid.ir/paper/230056/en

IEEE: Copy

AMIN RAEI, PARHAM PAHLAVANI, and MAHDI HASANLOU, “DETERMINING EFFECTIVE FACTORS ON FOREST FIRE USING THE COMPOUND OF GEOGRAPHICALLY WEIGHTED REGRESSION AND GENETIC ALGORITHM, A CASE STUDY: GOLESTAN, IRAN,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 3, no. 4, pp. 97–120, 2016, [Online]. Available: https://sid.ir/paper/230056/en

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