Document Type : پژوهشی

Authors

1 Ahmad, Najafi Eigdir, PhD Student of Geomorphology, University of Tabriz.

2 Geomorphology Professor, Department of Geomorphology, Tabriz University, Tabriz.

3 University of Tabriz.

4 The university of Tabriz

5 Assistant professor of geography, Soil Conservation and Watershed Management Research Institute, Tehran, Iran

Abstract

1-Introduction
Landslides are influential factors in human life that are not well-known. Several factors have contributed to the occurrence of landslide that could increase the risk of landslide in any area. Identifying these factors and their value can help to appropriate landslide zonation. The classification of areas susceptible to sliding and hazard zoning is an important step in assessing environmental hazards and plays an indelible role in the management of catchment areas (Sakar, 1995). Therefore, knowing the most important factors affecting slip instability and slipping will help us to make developmental plans using appropriate methods. Therefore, by using statistical models, their vulnerability to landslide is identified and zoned by assessing and validating them. Landslide inventory map is the best method for designing a landslide hazard map based on aerial photo interpretation, field surveys, and historic landslides. Then, the spatial distribution of mass movements is presented as a point or polygon on the map. The purpose of this research is to investigate various and effective factors in the occurrence of landslides, as well as to evaluate and compare the effectiveness of statistical models in landslide hazard zonation in Nazlochai basin and introducing the most appropriate methods.
2-Methodology
In order to investigate the landslide susceptibility zonation, the provision of a landslide inventory map is the most important part of the work, which can be done by using of geographic information systems with high accuracy. The accuracy of landslide zonation is largely dependent on this stage. So, at first, the existing landslides were identified by using various tools including aerial photos, satellite imagery (Google Earth), existing information, GPS, and in particular field surveys. In the present study, ten factors affecting the occurrence of landslides were considered: elevation, slope, gradient direction, distance from the waterway, distance from the road, distance from the faults,

 
lithology, land use, rainfall and vegetation index .For landslide zonation, bivariate statistical models, including Gupta-Joshi model with its correction method, information value method, and surface density method have been used.
3-Results and Discussion
For landslide hazard zonation using the Bivariate Statistical Models, various factors including elevation, slope, gradient direction, distance from the waterway, distance from the road, distance from fault, lithology, landuse, rainfall and vegetation index were studied. Existence and density of landslides in the western slopes show the role of geological formations, the distance from the waterway and precipitation in the occurrence of landslide. To evaluate the accuracy of the Bivariate Statistical Models, the density ratio index and the quality sum index were used. The more distinction between risk classes is, the model is more capable, and the quality sum index is used to compare the performance of different models. Finally, with respect to the resulting values, the zoning with the information value and surface density models were found to be desirable for the studied area.
4-Conclusion
According to the results of zoning (using the Bivariate Statistical Models), lithology, distance from the waterways and precipitation are the most important factors controlling the landslide occurrence in the studied area. Particularly lithologic factors are of great importance. Most of the landslides in the study area occurred on limestone and conglomerate, which are similar to the results of the research Amir Ahmadi who worked for Iran, while these formations do not have enough area in the basin. Limestone and a small amount of dolomitic limestone with an occupancy level of 15.5% of the basin, contain more than 30% of landslides. More importantly, limestone is coinciding with north orientation that confirms the role of gradient direction in occurrence of landslides. Although some scholars ignore the role of gradient direction (A. Gemitzi, 2011), other researchers (Carrara et al., 1991; Roostaei et al., 2017) have taken it into account in their research. The impact of the human factor mainly depends on changing environmental conditions, such as road construction, inappropriate plowing, excessive grazing and water diversion for agricultural use. Therefore, by studying the researches in Iran and in different parts of the world, the slipping factors in different basins and regions are not the same and in fact, different slip conditions are present in different regions.

Keywords

Abedini, M., & Fathi, M. H. (2014). Zoning of landslide risk sensitivity in Khalkhal Chay watershed using multi-criteria models. Quantitative Geomorphological Research, 2(4), 71-85.
Adhikari, M. (2011). Bivariate statistical analysis of landslide susceptibility in western Nepal. Master thesis in geosciences. University of Oslo. pp: 1-88.
Aleotti, P. & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bull EngGeolEnv, 58: 21-44.
Amir Ahmadi, A., Pourhashemi, S., Akbari, E. (2014). Selection of an    appropriate model among two-variable statistical methods for landslide   risk zoning in GIS environment (Case study: Baqieh watershed). Geographical Studies of Arid Areas, 15, 71-89.
Ayalew, L., Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1/2): 15–31.
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., Reichenbach P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16, 427–445.
Ebadinejad, S. A., Yamani, M., Maghsoudi, M., & Shadfar, S. (2007). Evaluating the efficiency of fuzzy logic operators in determining landslide capability (Case study of Shirood watershed). Iranian Journal of Watershed Management Science and Engineering, 1(2), 39-44.
Entezari, M., Izadi, Z. (2013). Study and evaluation of bivariate statistical methods in landslide risk zoning, Journal of Geography and Environmental Planning, No. 4,. 205-214.
Gemitzi, A., Falalakis, G. and Eskioglou, P. (2011). Evaluating landslide susceptibility using environmental factors, Fuzzy membership functions and GIS. Global Nest Journal, 13(1), 28-40.
Geological maps of Urmia and Sero with a scale of 1: 100000 and geological maps with a scale of 1: 250,000. Geological Survey of Iran, 1988 and 2006, along with reports.
Gupta, R. P & Joshi, B. C. (1990). Landslide hazard zoning using the GIS approach- a case study from Ramganga catchment, Himalayas. Engineering geology, 28, 119-131.
Kanungo, D. P et al., (2009). Landslide susceptibility zonation (LSZ) mapping – a review. Journal of south Asia disaster studies, 2 (1), 81-105.
Karimi, H., Naderi, F., Morshedi, E., & Nikseresht M. (2011). Landslide Hazard Zoning in Chardavol Watershed in Ilam Using Geographic Information System (GIS). Journal of Applied Geology, 7(4), 319-332.
Karimi Sangochin, I., Onagh, M., & Saad al-Din, A. (2012). Comparison of the efficiency of 4 quantitative and semi-quantitative models of landslide risk zoning in Chehelchai watershed, Golestan province. Journal of Soil and Water Conservation Research, 19 (1), 183-196.
Magliulo, P. et al., (2008). Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics, a case study in southern Italy. Nat Hazards 47, 411-435.
Meteorological Studies Report. (2006). General Department of Natural Resources of West Azerbaijan, Chaharmahal Water Rescue Consulting Engineers.
MirNazari, J., Shahabi, H., & Khezri, S. (2014). Currency and Physics of the AHP and Objectives in the Islamic Republic of Iran. Journal of Geography and Development, 37, 53-70.
Mosfaei, J., Onagh, M., & Mesdaghi, M. (2009). Comparison of the efficiency of experimental and statistical models of landslide risk zoning (Case study: Alamut River watershed). Journal of Soil and Water Conservation Research, 16(4), 43-61.
Rahimpour, T., Roostaei, S. & Nakhostinrouhi, M. (2018). Landslide Hazard Zonation Using Analytical Hierarchy Process and GIS. A Case Study of Sardool Chay Basin, Ardabil Province. Hydrogeomorphology, 4(13), 1-20.
Roostaei, S., Khairizadeh, M., Sarafrozeh, S., & Najafi Igdir, A. (2012). Landslide risk zoning using the factor of uncertainty model. National Conference of the Iranian Geomorphological Association, 1, 163-165.
Roostaei, S., Najafi Igdir, A., & Hejazi, A. (2018). Landslide Hazard Zonation Using the Fuzzy Logic Method in Nazlo-Chay Basin. Quantitative Geomorphological Research, 6(4), 103-119.
Sakar, S., Kanungo, D.P. and Mehrotra, G.S., (1995). Landslide hazard zonation. A case study in Garhwal Himalaya, India. Mountain Research and Development, 15(4), 301–309.
Saha, A. K et al., (2005). An approach for GIS-based statistical landslide susceptibility zonation-with a case study in the Himalayas. Landslides 2, 61-69.
Shadfar, S., Nowruzi, A. A. Ghodoosi, J., and Jafar Gh. (2005). Landslide Hazard Zoning in Laktrashan Watershed, Soil and Water Conservation Extension Scientific Journal, 1, 1-10.
 Teimouri, M., & Asadi Nalivan, O. (2020). Susceptibility Zoning and Prioritization of the Factors Affecting Landslide Using MaxEnt, Geographic Information System and Remote Sensing Models (Case study: Lorestan Province). Hydrogeomorphology, 6(21), 155-179.
Van Westen, C. J & Soeters, R. (1998). Geographic information systems in slope instability zonation (GISSIZ), ITC, P. 156.
Van Westen, C. J. (1997). Statistical landslide hazard analysis. ILWIS 2.1 for windows applications guide. ITC publication, Enschede, pp. 73-84.
Wang, K. L. and Meei-Ling L. (2010). Development of shallow seismic landslide potential map based on newmark's displacement. The case study of Chi-Chi earthquake, Taiwan, Environ Earth Sci, 60, 775-785.
Yin K, J. and Yan T. Z. (1988). Statistical prediction model for slope instability of metamorphosed rocks. Proceeding of the 5th international symposium on landslides, Lausanne, Switzerland 2, 1269-1272.