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Manuscript ID : 528629 Visit : 109 Page: 123 - 140

20.1001.1.20085656.1395.9.32.8.2

Article Type: Original Research

Evaluation of prediction capability of the Statistical and Logestic models for mapping landslide susceptibility (Case Study: Vanakbasin )

Subject Areas : Climatology

Alireza Arab Ameri 1 ( PhD Student Geomorphology, Tarbiat Modarres University, Tehran, Iran )
Koorosh Shirani 2 ( Assistant Prof. Research Center for Agriculture and Natural Resources, Isfahan, Iran )
Amir Hosein Halabiyan 3 ( Associate Prof. Dep. of Geography, Payam Noor University, Tehran, Iran )

Received: 2015-11-02 Accepted : 2016-12-28 Published : 2016-09-21

Keywords: Evaluation, landslide, Statistical models, quality sum index, Vanak basin,

Abstract :

The aim of this study is to produce landslide susceptibility mapping by Statistical models based on geographic information system (GIS) in the Southwestern of Isfahan Province,Vanak basin. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 140 cases (70 %) out of 200 detected landslides were randomly selected for modeling, and the remaining 60 (30 %) cases were used for the model validation. The landslideconditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, density of streams, distance to road and land use were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by logistic regression, density area and Certainty Factor models. The results of the models assessment showed that area density method by applying quality sum index (QS) is the highest value (0.35), then certainty factor and Logestic Regression are values of 0.29 and 0.11 in the next category, respectively. The interpretation of the susceptibility map indicated that altitude, rainfall and slope aspect play major roles in landslide occurrence in the study area These landslide susceptibility maps can be used for planning of land use , future road construction and hazard mitigation purpose.

References:


  1. بلورچی، م، انصاری، ف، (۱۳۷۸): سنگ افت در روستای آبیکار در استان چهارمحال و بختیاری، کنفرانس زمین شناسی مهندسی و محیط، دانشگاه تربیت مدرس، صص ۵۹-۶۰.
    2.
  2. سپهر، ع، بهنیافر، ا، محمدیان، ع.ع، عبداللهی،ا، 1392: تهیه نقشه حساسیت پذیری زمین لغزش دامنه های شمالی بینالود بر پایه الگوریتم بهینه سازی توافقی ویکتور، پژوهش های ژئومورفولوژی کمی، شماره 1، صص. 36-19.
    3.
  3. شیرانی، ک، عرب عامری، ع.ر، (۱۳۹۴): پهنه بندی خطر وقوع زمین لغزش با استفاده از روش رگرسیون لجستیک مطالعه موردی: دز علیا، مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، شماره ۷۲، صص ۳۳۴-۳۲۱.
    4.
  4. عرب عامری، ع.ر، کلوراژان، ع، کرمی، ج، علیمرادی، م، شیرانی، ک، (۱۳۹۳): استفاده از شبکه عصبی در پهنه بندی خطر زمین لغزش مطالعه موردی: حوضه ماربر، نشریه پژوهشی تحلیلی زمین پویا، شماره ۲، صص ۱۱۲-۱۲۸.
    5.
  5. عربعامری،ع.ر،حلبیان، ا.ح، (۱۳۹۴):پهنهبندیخطرزمینلغزشبااستفادهازمدلآماریدو ‏متغیرهوزنی ‏AHP‏ وسیستماطلاعاتجغرافیایی (مطالعهموردی: حوضهزرند)،جغرافیایطبیعی،شماره ‏‏۲۸،صص۸۶-۶۵.‏
    6.
  6. عربعامری،ع.ر،شیرانی،ک، (۱۳۹۵): اولویتبندیعواملموثردرزمینلغزشوپهنهبندیخطرآنبا ‏استفادهازتئوریاحتمالاتیدمپسترشفر (مطالعهموردی: حوضهونک،استاناصفهان)،مهندسیومدیریت ‏آبخیز،سالهشتم،شماره۱،صص۱۰۶-۹۳.‏
    7.
_||_

  1. Akgu¨n A., Kıncal, C. and Pradhan, B. 2011. Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess. doi:10.1007/s10661-011-2352-8.
    2.
  2. Alexakis, D.D., Agapiou, A., Tzouvaras, M., Themistocleous, K., Neocleous, K., Michaelides, S., Hadjimitsis, D.G., 2013. Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Nat. Hazards 72 (1), 119–141.
    3.
  3. Bednarik, M., Magulova, B., Matys, M. and Marschalko, M. 2010. Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35(3–5):162–171.
    4.
  4. Binaghi, E., L. Luzi, P. Madella, F. Pergalani andA. Rampini. 1998. Slope instability zonation: a comparison between certainty factor and fuzzy dempster.shafer approaches. Natural Hazards, 17: 77-97.
    5.
  5. Burrough, P.A. and McDonnel, R. 1998. Principles of Geographical Information Systems. 2nd ed. Oxford University Press, London, 193p.
    6.
  6. Concha-Dimas, A., M. Campos-Vargas and, C. Lopez-Miguel. 2007. Comparing Heuristic and Bivariate GIS-based Methods  for  Refining Landslide Susceptibility  Maps  in  Northern  Mexico  City.Environmental  & Engineering Geoscience, 13: 277- 287.
    7.
  7. Can, T., H.A. Nefeslioglu, C. Gokceoglu, H. Sonmez and Y. Duman. 2005. Susceptibility Assessment of Shallow Earth Flows Triggered by Heavy Rainfall at three Catchments by Logistic Regression Analysis, Geomorphology, 82: 250-271.
    8.
  8. Chang, K.T. and S.H. Chiang. 2007. Modeling Typhoon and Earthquake induced Landslide in a Mountainous Watershed using Logistic Regression. Geomorphology, 89: 335-347.
    9.
  9. Chen, X.L., Liu, C.G., Yu, L., Lin, C.X., 2014. Critical acceleration as a criterion in seismic landslide susceptibility assessment. Geomorphology 217, 15–22.
    10.
  10. Choi, J., Oh, H.J., Lee, H.J., Lee, C. and Lee, S. 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23
    11.

  1. Dai, F.C. and C.F. Lee. 2002. Landslide characteristics and slop instability modeling usingGIS, Lantau Island, Hong Kong, Geomorphology. 31‎‏:‏‎181-216.‎
    2.
  2. Girma, F., Raghuvanshi, T.K., Ayenew, T., Hailemariam, T., 2015. Landslide hazard zonation in ‎Ada Berga District, Central Ethiopia – a GIS based statistical approach. J. Geomatics 90, 25–38 (i).‎
    3.
  3. Lepore, C., Kamal, S.A., Shanahan, P. and Bras, R.L. 2011. Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environmental Earth Sciences. doi:10.1007/s12665-011-0976-1.
    4.
  4. Nampak, H., Pradhan, B., Manap,M.A., 2014. Application of GIS based data driven evidential ‎belief function model to predict groundwater potential zonation. J. Hydrol. 513, 283–300.‎

    1. Yu, X., Wang, Y., Niu, R. and Hu, Y. 2016. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China, Int J Environ Res Public Health, 13 (5), doi:  10.3390/ijerph13050487.
      2.


  1. Dahal, R.K., S.H. Hasegawa, A. Nonoura, M. Yamanka, S.  Dhakal and, P. Paudyal,  2008.  Predictive  modeling  of  rainfall-  induced  landslide hazard  in  the  lesser  Himalaya  of Nepal based on weights of evidence. Geomorphology 102, 496- 510.
    2.
  2. Devkota, K C., Regmi, A. D., Pourghasemi, H .R., Yoshida, K., Pradhan, B., Ryu, I. C., Dhital, M. R. and Althuwaynee, O.F. 2012. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya; Nat. Hazards, doi: 10.1007/s11069-012-0347-6.
    3.
  3. Gee, M.D. 1992. Classification of landslides hazard Zonation methods and a test of predictive capability, Bell, Davi, H(Ed), Proceedings 6th International Symposium on Landslide, 56-48.
    4.
  4. Gee, M.D. 1991. Classification of landslide hazard zonation methods and a test of predictive capability, Landslides, Bell (ed.), 1991 Balkema, Rotterdam: 947- 952.
    5.

  1. Gorsveski, P.V., Gessler, P.E., Boll, J., Elliot, W.E. and Foltz, R.B. 2006. Spatially and tem - porally distributed modeling of landslide susceptibility, Geomorphology,  80: 178–198.
    2.
  2. Guzzetti, F., M. Cardinali, P.  Relchenbach and A.  Carrara. 2000. comparing landslide map: A case study in the upper Tiber river basin, central Italy. Environmental Management,  25: PP. 247-263.
    3.
  3. Hamza, T., Raghuvanshi, T.K. GIS based landslide hazard evaluation and zonation – A case from Jeldu District, Central Ethiopia.Journal of King Saud University – Science (2016), http://dx.doi.org/10.1016/j.jksus.2016.05.002.
    4.
  4. Hansen,  A.  1984. Engineering geomorphology: the application of an evolutionary model of Hong Kong, Zeitschrift für Geomorphologie, 51: 39–50.
    5.
  5. Hong, H., Pourghasemi, H.R., Pourtaghi, Z.S. 2016. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models, Geomorphology 259 (2016) 105–118.
    6.
  6. Ilinca, V. and Gheuca, I. 2011. The red lake landslide (Uciga؛uMountain, Romania). Carpathian Jour. Earth Environ. Sci.,v, 6(1); 263-272.
    7.
  7. Jade, S. and S. Sarkar. 1993. Statistical models for Slope Instability classifications Engineering, Geology, 36:  PP. 91-98.
    8.
  8. Lee, S. and B. Pradhan. 2007. Landslide Hazard Mapping at Selangor Malaysia using Frequency Ratio and Logistic Regression models. Landslide, 4: 33-41.
    9.
  9. Lee, S.A. 2004. Verification of spatial logisticregression for landslide susceptibilityanalysis: Acasestudy of Korea. Geomorphology, 44: 15-18.
    10.
  10. Lee, S.and M. kyungduck. 2001. Statistical analysis of landslide susceptibility at Yonging, Korea. Enviromented Geology. 40: 1095–1113.
    11.

  1. Oh, H.J. and Lee, S. 2010. Cross-validation of logistic regressionmodel for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Advances, 3: 44–55.
    2.
  2. Oh, H.J., and Lee, S. 2011a. Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environmental Earth Sciences 62: 935–951.
    3.
  3. Oh, H.J. and Lee, S., 2011b. Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environmental Earth Sciences, 64: 395–409.
    4.
  4. Peng, L., Niu, R., Huang, B.,Wu, X., Zhao, Y., Ye, R., 2014. Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204, 287–301.
    5.
  5. Pourghasemi, H.R., Kerle, N., 2016. Random forest-evidential belief function based landslide susceptibility assessment in western Mazandaran Province, Iran. Environ. Earth Sci. 75, 185.
    6.
  6. Pradhan, B. and Youssef, A.M. 2010. Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326
    7.
  7. Pradhan, B., Abokharima, M.H., Jebur, M.N., Tehrany, M.S., 2014. Land subsidence susceptibilitymapping at Kinta Valley (Malaysia) using the evidential belief function modelin GIS. Nat. Hazards 1-24.
    8.
  8. Regmi, N. R., Giardino, J. R. and Vitek, J. D. 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115:172-187.
    9.
  9. Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T., Akgun, A. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya, Arab J Geosci , 7:725–742
    10.
  10. Suh, J., Choi, Y., Roh, T.D., Lee, H.J. and Park, H.D. 2011. National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environmental Earth Sciences, 63: 619–632.
    11.
  11. Van Westen, C.J., N. Rengers and R. Soeters. 2003. Use of Geomorpho-logical Information in Indirect Landslide Susceptibility Assessment.  Natural Hazards, 30: 399- 419.
    12.
  12. Yesilnacar, E. and Topal, T. 2005. Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engg. Geol, 79: 251-261.
    13.
  13. Yilmaz, I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences 61: 821–836.
    14.
  14. Youssef, A.M., Pourghasemi, H.R., El-Haddad, B.A., Dhahry, B.K., 2015a. Landslide susceptibility mapping using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull. Eng. Geol. Environ. http://dx.doi.org/10.1007/s10064-015-0734-9.
    15.
  15. Youssef, A.M., Pourghasemi, H.R., Pourtaghi, Z., Al-Katheeri, M.M., 2015b. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir regio, Saudi Arabia. Landslides. http://dx.doi.org/10.1007/s10346-015-0614-1.
    16.
  16. Youssef, A.M., Pradhan, B., Pourghasemi, H.R., Abdullahi, A., 2015c. Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS. Geosci. J. 19 (3), 449–469.
    17.

  1. Zhu, A., Wang, R., Qiao, J., Qin, C.Z., Chen, Y., Liu, J., Du, F., Lin, Y., Zhu, T., 2014. An expertknowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214, 128–138.
    2.

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