Investigation of Effective Factors on Landslide Occurrence and Susceptibility Zonation Using the Dempster-Shafer Model in the Middle Mazlaghan Chai, Markazi Province

Document Type : Research

Author

Lecturer of Geography Department, Pyame Noor University, Iran

10.22092/wmrj.2023.360607.1502

Abstract

Introduction and Goal
Middle Mazlaghan Chai watershed located in west of Saveh county. The activity of faults and the presence of sensitive rocks have created suitable conditions for the occurrence of small and large landslides. One of the important strategies for reducing losses caused by landslides is avoiding high-risk and very high-risk areas. For this purpose, it is necessary to prepare a relatively accurate landslide susceptibility zoning map from among the existing methods. One of the best methods for landslide zonation is the Dempster-Shafer model. The purpose of this research is determination of the factors affecting the occurrence of landslide, presentation and evaluation of the landslide susceptibility zonation map using the Dempster-Shaffer method and area under curve (AUC), respectively in Middle Mazalaghan Chai.
Materials and Methods
The Middle Mazlaghan Chai watershed, with an area of ​​21,746 ha, is located in Markazi Province. Maximum elevation is 2833 m above sea level in the northern heights of the watershed and minimum elevation is 1399 m at the outlet of the Bivaran River. The average annual temperature of the study area is 13 °C and the average annual precipitation varies from 246 mm in the south to 500 mm in the north. The climate of the region is arid and semi-arid. 52% of the soils in the study area are in the type of entisol and without profile development, and 37% of the vegetation cover consists of moderate and poor rangeland. Initially, a landslide inventory map was prepared in environment of geographic information system using fieldworks, aerial photography and satellite imagery. Then using fieldworks and related research, the most important factors affecting landslides in the study area, including slope, aspect, elevation, distance to fault, distance to road, distance from stream, land-use, lithology and precipitation were investigated and determined. After preparing the information layers and weighting in the GIS, a landslide susceptibility zonation map was prepared and classified using Dempster Shafer method. Finally, the efficiency of the Dempster Shafer method was evaluated using the area under curve (AUC).
Results and Discussion
Fossiliferous limestone units and tuff and lava alternations showed the highest susceptibility to landslides. The results of this research showed the most landslides occurred more than 100 m from streams and at distances greater than 200 m from the roads and faults. Slopes more than 40% are most susceptible to landslides. The northern aspect, elevation more than 2600 m, and precipitation more than 450 mm also showed the highest susceptibility to landslides in the studied area. The zoning results showed that about 22% of the study area is located in high and very high susceptibility zones and approximately 75% of landslides occurred in high and very high susceptibility zones. The area under the curve of the landslide susceptibility zonation map was also obtained 0.849.
Conclusion and Suggestions
Accurate identification of landslide locations using GPS device is one of the results obtained from this research. As a result, the landslide distribution map of 192 landslides was prepared and it was checked and recorded for the first time in the study area. Among the lithological units, fossiliferous limestone units and alternations of tuff and lava showed the highest susceptibility to landslide, and the landslide susceptibility zones are in complete relation to the geological structures of the region. It is suggested that this model be compared with other models and the results of this research will be used as basic information for environmental management and planning.   

Keywords


Arabameri A, Shirani K. 2016. Identification of effective factors on landslide occurrence and its Hazard ‎zonation using Dempster-Shafer theory, Case study: Vanak Basin, Isfahan ‎Province. Watershed Engineering and Management. 8(1): 93-106. (In Persion).  https://doi.org/10.22092/ijwmse.2016.105975
Arabameri A, Shirani K, Rezaei K. 2017. A Comparative Assessment between weights-of-evidence and frequency ratio models for landslide Hazard zonation in Vanak Basin. Journal of Watershed Management Research. 8(15):147-160. (In Persion).  doi:10.29252/jwmr.8.15.147
Azimpour Moghadam V. 2015. Landslide risk zoning using bayesian and Dempster-Shafer theory (Case study: A part of Babolrud Watershed). Master's Thesis. Faculty of Agricultural Sciences and Natural Resources. University of Sari. 135 p (In Persion).
Chen W, Pourghasemi HR, Zhao Z. 2016. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International. 32(4): 367-385 DOI:10.1080/10106049.2016.1140824
Crosta G, Clague JJ. 2009. Dating, triggering, modeling, and Hazard assessment of large landslides. Geomorphology. 103(1):1-4   DOI: 10.1016/j.geomorph.2008.04.007
Devkota K, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu CY, Dhital MR, Althuwayee OF. 2013. Landslide susceptibility mapping using certainty factor, Index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road ection in Nepal Himalaya. Natural Hazards. 65(1):135–165 DOI:10.1007/s11069-012-0347-6
Hoseinpour Milaghardan A, Delavar M, Chehreghan A. 2016. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory, model. Earth Syst. Environ. 2(4):1-10.  DOI:10.1007/s40808-016-0240-5
Khaledi S, Derafshi K, Mehrjounezhad A, Gharch Chahi S. 2012. Assessment of the landslide effective factors and zonation of this event using logistic regression in the GIS environment: The Taleghan Watershed Case Study. Journal of Geography and Environmental Hazards. 1(1): 65-82 (In Persian). 10.22067/geo. v1i1.16523
Komac M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in per alpine Slovenia, Geomorphology. 74(1):17-28.  DOI: 10.1016/j.geomorph.2005.07.005
Mahmoudi F. 2007. Dynamic Geomorphology. Payameh Noor University. 326 p. (In Persian).
Moghimi E, Alavipanah SK, Timuri J. 2009. Evaluation and zonation of effective factors on landslide occurrence of Aladagh northern slopes (Case study: Chenaran drainage basin in north Khorasan Province). Geographical Research Journal. 40(64): 53-75. (In Persian).
Moradi HR, Mohammadi M, Pourghasemi HR, Mustafa Zadeh R. 2010. Landslide risk analysis in Golestan province using Dempster-Shafer theory. Researches in Earth Sciences. 1(3): 1-14
Park NW. 2011. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis.  Environmental Earth Science. 62(2): 367-376.  DOI:10.1007/s12665-010-0531-5
Pourghasemi HR. 2007. Landslide risk assessment using fuzzy logic (Case study of part of Haraz Watershed). Master Thesis, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University. 92 p. (In Persion).
Pourghasemi HR, Moradi HR, Fatemiaghda S, Gokceoglu C, Pradhan, B. 2013. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran). Arabian Journal of Geosciences. 7(5): 1857-1878.  DOI:10.1007/s12517-012-0825-x
Roostaei S, Jananeh C. 2020. Slope instability hazard zonation in Baleghluchai watershed in Ardabil using AHP Fuzzy method. The Journal of Geography and Planning.  23(70): 169-188
Rowshanzamir S. 2008, Research project, Investigation of effective factors on landslides in Maragheh Saveh Basin. Payame Noor University. 74 p. (In Persian). 
Saberchenari K, Soleimani H, Maryam Sadat Mirabdini MS. 2017. Landslide risk zoning using Demester-Shafer theory, Case study: Ziarat Watershed, Golestan Province. Geological Engineering. 11(4): 385-404. (In Persian).
Shariat jafari M.1996. Landslide (Basics and principles of Stability of natural slopes). Soil Conservation and Watershed Management Research Institute. 218p . (In Persian).
Servati MR, Hosseinzadeh MM, Khezri S, Mansouri A. 2008, The zoning of mass movement in Sanandaj-Dehgolan road using analytical hierarchy process (AHP). Geographical Data (SEPEHR). 17(68): 25-32 (In Persian).
Shirani K, Chavoshi S,Ghayoumian J. 2006. Investigation and evaluation of landslide risk zoning methods in semirom upper Padna. Research Journal of University of Isfahan "Science". 23(1): 23-38. (In Persian).
Shirani K, Pasandi M, Arabameri A. 2018. Landslide susceptibility assessment by Dempster-Shafer and index of entropy models, Sarkhoun Basin, Southwestern Iran. Natural Hazards. 93(3): 1379-1418    DOI:10.1007/s11069-018-3356-2
Taghavimoghadam E, Kalali Moghadam Z, Pourhashemi S, Motamedi Rad M. 2013, Landslide risk zoning in mirabad neyshabour basin using the method AHP in GIS invironment. 8th Conference of the Iranian Association of Engineering Geology and the Environoment. 16p (In Persian).
Wang Q, Li W, Wu Y, Pei Y, Xing M, Yang D. 2016. A comparative study on the landslide susceptibility mapping using evidential belief function and weight of evidence models. Journal of Earth System Science. 125(3): 646-662. DOI:10.1007/s12040-016-0686-x
Xu C, Xu X, Dai F, Saraf AK. 2012. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers and Geosciences. 46: 317-329.     DOI: 10.1016/j.cageo.2012.01.002
Yalcin A, Reis S, Aydinoglu A, Yomralioglu T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena. 85(3): 274-287.
Yesilnacar EK. (2005). The Application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis Department of Geomatics the University of Melbourne. 423 p.
Youssef AM, Pourghasemi HR, EI-Haddad BA, Dhahry BK. 2016. Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull Eng Geol Environmental. 75(1): 63-87. ‎ DOI:10.1007/s10064-015-0734-9