Forest fire hazard map using Artificial Neural Network in Golestan Province

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FEGHHI J.; Alimahmoodi Sarab S.; Khaje S.Gh.

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Journal: Journal of Wood and Forest Science and Technology Year:2018 | Volume:25 | Issue:2 Page(s): 123-136

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Title

Forest fire hazard map using Artificial Neural Network in Golestan Province

Author(s)

FEGHHI J. | Alimahmoodi Sarab S. | Khaje S.Gh. | Issue Writer Certificate

Keywords

ANNQ1

Fire potentialQ1

GISQ1

Golestan provinceQ1

Abstract

Background and objectives: In preventing fire and reducing their effects, forest fire management is important. The purpose of this study was to provide a map of fire hazard potential and determine the effective factors on forest fires using GIS and artificial neural network for the cities of Ali-Abad, Ramayan, Azadshahr, Minoodasht and Kalaleh in Golestan province. Materials and methods: The criteria used in this research included forest and rangeland lands, temperature, rainfall and evapotranspiration, slope, aspect, elevation of the sea level, distance from urban areas, distance from rural areas, distance from agricultural land and distance from the road. Vegetation map, digital elevation map and land use map provided from the forests of the rangelands and watersheds of the country, and the maps of temperature, rainfall, and evapotranspiration obtained from the organization of the meteorological for the whole of Iran. The influence of each parameter on fire ignition was determined by collecting of 37 samples from burned area and 37 sample from not burned area. 15 fire points that were not used in the modeling process was used For Validation the potential fire hazard map. For formation network between criteria and fire occurrence used of Multilayer perception (MLP) with Hyperbolic Pattern Algorithms. To this end, 70% of the data was used to train the network, 15% of the tests were tested and 15% of the data were used to validate the results. Results: The results were shown raining and distance from the road had must be influenced on forest fire ignition. Validation test showed that the best network was obtained in run 4 and epoch 450 with 0. 0038 Final Mean Square Error (FMSE) in training steps. Furthermore, about 95 percent of area burned and 84 of unburned areas has been properly classified. Finally, forest fire hazard maps was obtained based on each criteria weight. Results showed this network with 2 hidden layers and 12 neurons in each of them has best accuracy, and correlation coefficient (R) was 0. 80. Furthermore, after determining the location of the 15 forest fire points on the map of potential fire hazard, the results showed that 7 points located in the area were very high fire risk, and 6 points in the area with a high fire risk, and 2 points located in the area with a low fire hazard. Conclusion: According to the findings of this research, roads and rain reduction have a growing impact on the development of fire and natural forest fire managers need to adoption the necessary measures to better manage the forest and rangeland areas in these situations to prevent the occurrence or spread of fire.

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APA: Copy

FEGHHI, J., Alimahmoodi Sarab, S., & Khaje, S.Gh.. (2018). Forest fire hazard map using Artificial Neural Network in Golestan Province. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, 25(2 ), 123-136. SID. https://sid.ir/paper/406712/en

Vancouver: Copy

FEGHHI J., Alimahmoodi Sarab S., Khaje S.Gh.. Forest fire hazard map using Artificial Neural Network in Golestan Province. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY[Internet]. 2018;25(2 ):123-136. Available from: https://sid.ir/paper/406712/en

IEEE: Copy

J. FEGHHI, S. Alimahmoodi Sarab, and S.Gh. Khaje, “Forest fire hazard map using Artificial Neural Network in Golestan Province,” JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, vol. 25, no. 2 , pp. 123–136, 2018, [Online]. Available: https://sid.ir/paper/406712/en

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