LANDSLIDE HAZARD ZONATION USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: SEPIDDASHT- LORESTAN, IRAN)

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BAHARVAND S.; SOORI S.

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Journal: Journal of RS and GIS for Natural Resources Year:2016 | Volume:6 | Issue:4 Page(s): 15-31

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Information Journal Paper

Title

LANDSLIDE HAZARD ZONATION USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: SEPIDDASHT- LORESTAN, IRAN)

Author(s)

BAHARVAND S. | SOORI S. | Issue Writer Certificate

Keywords

LANDSLIDEQ2

ARTIFICIAL NEURAL NETWORK (ANN)Q2

GEOGRAPHIC INFORMATION SYSTEM (GIS)Q2

SEPIDDASHTQ2

LORESTANQ2

Abstract

This study was carried out to determine the relative hazard zonation of the slope instabilities and LANDSLIDE occurrence in SEPIDDASHT, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorithm were used. In order to study the stability of the slopes, the LANDSLIDEs of the region were initially identified and recorded using satellite images of TM and ETM+, aerial images of 1: 50, 000, and field surveys (year, 2014). The impact of each factor including slope, aspect, land use, elevation, lithology, precipitation, the distance from the fault road and drainage on the slope instabilities was estimated using the ArcGISÒ10.1 software via combining the map of the factors influencing the LANDSLIDE with the LANDSLIDE distribution map. Then a proper structure (1-13-9) for the LANDSLIDE hazard zonation of SEPIDDASHT region was obtained through training the artificial neural network by MATLAB software. Based on the results of the LANDSLIDE hazard zonation, 0.18, 12.41, 14.09, 29.85, and 43.52 percent of the region were located in very low, low, medium, high, and very high risk classes respectively.

Cites

EVALUATION OF FOUR ALGORITHMS FOR ESTIMATION OF CANOPY COVER OF MANGROVE FORESTS BY USING AERIAL IMAGERY

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APA: Copy

BAHARVAND, S., & SOORI, S.. (2016). LANDSLIDE HAZARD ZONATION USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: SEPIDDASHT- LORESTAN, IRAN). JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 6(4), 15-31. SID. https://sid.ir/paper/189512/en

Vancouver: Copy

BAHARVAND S., SOORI S.. LANDSLIDE HAZARD ZONATION USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: SEPIDDASHT- LORESTAN, IRAN). JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2016;6(4):15-31. Available from: https://sid.ir/paper/189512/en

IEEE: Copy

S. BAHARVAND, and S. SOORI, “LANDSLIDE HAZARD ZONATION USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: SEPIDDASHT- LORESTAN, IRAN),” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 6, no. 4, pp. 15–31, 2016, [Online]. Available: https://sid.ir/paper/189512/en

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