Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony

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TAHERI VALEH; KARAM AMIR; SAFARI AMIR; Shataii Joibari Shaban

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Journal Paper

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Journal: Geographical Planning of Space Year:2020 | Volume:10 | Issue:37 Page(s): 213-230

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Information Journal Paper

Title

Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony

Author(s)

TAHERI VALEH | KARAM AMIR | SAFARI AMIR | Shataii Joibari Shaban | Issue Writer Certificate

Keywords

Landslide

Zoning

Artificial colonies

Least squares support vector machines

Golestan province

Abstract

Landslide Zoning based on the risk of Landslide hazard using one of the proper algorithms is one of the land management methods. So far, the effectiveness of many algorithms and algorithms for Landslide hazard zonation has been investigated in order to use the best and most efficient method and algorithm. In this research, the efficiency of the newest least squares algorithm for support and colony of artificial bison was evaluated in Golestan Landslide zonation. The research method was based on quantitative and analytical method and in the environment of two software GIS and MATLAB and 13 parameters including slope, tilt direction, digital elevation model, distance from fault, land use, geology, landscape, gender and type of soil, Valley Depth, Water Power Index, Earth Hardness Index, Moisture Index, Land Curvature, were selected for implementation of the model. The results indicate that the overall accuracy criterion for educational and evaluation data is 84. 4% and 81. 4%, respectively, which indicates that the mentioned model is validated in terms of validity and validity of modeling. Finally, the study area was categorized into five classes of very high, high, moderate, very low and low sensitivity. All of the results of the evaluation showed high performance and good predictive capacity of the least squares model of artificial support binoculars in identifying areas with high slip potential. Which can be used for better management in Golestan province. landslidSlide classification is 70. 57% in very low class, 0. 46% in low class, 15. 44% in middle class, 3. 61% in high class and 9. 41% in very high class.

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APA: Copy

TAHERI, VALEH, KARAM, AMIR, SAFARI, AMIR, & Shataii Joibari, Shaban. (2020). Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony. GEOGRAPHICAL PLANING OF SPACE, 10(37 ), 213-230. SID. https://sid.ir/paper/951176/en

Vancouver: Copy

TAHERI VALEH, KARAM AMIR, SAFARI AMIR, Shataii Joibari Shaban. Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony. GEOGRAPHICAL PLANING OF SPACE[Internet]. 2020;10(37 ):213-230. Available from: https://sid.ir/paper/951176/en

IEEE: Copy

VALEH TAHERI, AMIR KARAM, AMIR SAFARI, and Shaban Shataii Joibari, “Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony,” GEOGRAPHICAL PLANING OF SPACE, vol. 10, no. 37 , pp. 213–230, 2020, [Online]. Available: https://sid.ir/paper/951176/en

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