GIS-Based Landslide Susceptibility Mapping in Qazvin Province of Iran
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TECHNICAL NOTE
GIS‑Based Landslide Susceptibility Mapping in Qazvin Province of Iran Reza Arjmandzadeh1 · Ebrahim Sharifi Teshnizi2 · Ahmad Rastegarnia3 · Mohsen Golian4 · Parisa Jabbari4 · Husain Shamsi5 · Sima Tavasoli6 Received: 1 March 2019 / Accepted: 28 November 2019 © Shiraz University 2019
Abstract Landslides pose serious life and property losses annually around the world. In the present research, the analytical hierarchy process (AHP) is applied for landslide hazard zonation of Qazvin Province, Iran. Qazvin Province, located in the Central Basin of Iran with an area of 15,821 km2, occupies 1% of Iran’s total area. This province is located between longitudes 48° 45′ to 50° 50 E and latitudes 35° 37′ and 36° 45′ N. In the present work, the effect of geomorphological (slope and aspect), geological (lithology, slope difference, strata slope, earthquake acceleration, ground aspect difference, and strata aspect), and engineering geology (point load index, geological strength index, specific gravity, cohesion, internal friction angle, weathering, and condition of discontinuities) parameters is investigated on landslide occurrence through the AHP method. After analysis of the obtained data in ArcGIS software, the effect of each information layer on landslide occurrence was determined and zoning was done. The results obtained by comparing the prepared zoning map and distribution of the occurred landslides show the high accuracy of the AHP method for landslide zoning in Qazvin Province. Keywords Engineering geology · Landslide hazard · Analytical hierarchy analysis (AHP)
1 Introduction The mass movement of group materials not only results in geomorphological changes of slopes but also involves life and property loss. Many methods have been proposed for the zonation of slides and mass movements. In the following, some of the studies conducted in this subject are presented. Several methods such as support vector machines (SVM) (Marjanović et al. 2011), logistic regression (LR), SVM and * Ahmad Rastegarnia [email protected] 1
Department of Geology, Payame Noor University of Tehran (PNU), P.O.BOX 19395‑3697, Tehran, Iran
2
Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
3
Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran
4
Master of Science in Hydrogeology, Science and Research University of Tehran, Tehran, Iran
5
Department of Civil Engineering, Faculty of Engineering, University of Hormozgan, Hormozgan, Iran
6
Department of Geography, Faculty of Literature, Ferdowsi University of Mashhad, Mashhad, Iran
multi-criteria decision analyses (MCDA) (Kavzoglu et al. 2014), AHP and artificial neural network (ANN) (Quan and Lee 2012), spatial data infrastructures (SDI) and GIS (Fernández et al. 2013), fuzzy logic, and AHP (Pourghasemi et al. 2012) have been used for landslide susceptibility mapping in the previous studies. Zhang et al. (2016) assessed the landslide characteristics in the Koshi River basin, centr
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