تعداد نشریات | 161 |
تعداد شمارهها | 6,533 |
تعداد مقالات | 70,506 |
تعداد مشاهده مقاله | 124,127,173 |
تعداد دریافت فایل اصل مقاله | 97,234,932 |
Landslide susceptibility mapping using logistic regression analysis in Latyan catchment | ||
Desert | ||
مقاله 9، دوره 22، شماره 1، خرداد 2017، صفحه 85-95 اصل مقاله (780.26 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jdesert.2017.62181 | ||
نویسندگان | ||
A. Kouhpeima* 1؛ S. Feyznia2؛ H. Ahmadi3؛ A.R. Moghadamnia4 | ||
1Young Researchers and Elite Club, Islamic Azad University, Karaj Branch, Karaj, Iran. | ||
2Tehran Univercity | ||
3university of Tehtan, Iran | ||
4University of Tehran, Iran | ||
چکیده | ||
Every year, hundreds of people all over the world lose their lives due to landslides. Landslide susceptibility map describes the likelihood or possibility of new landslides occurring in an area, and therefore helping to reduce future potential damages. The main purpose of this study is to provide landslide susceptibility map using logistic regression model at Latyan watershed, north Iran. In the first stage, 208 Landslide locations were identified and mapped using extensive field surveys. 75 % of these landslides were used for training and 25 % of them for validation of the model. The mapped landslides were then georeferenced using ArcGIS 10 to provide the landslide inventory map. In the second stage, maps of factors affecting the occurrence of landslides were prepared in ArcGIS 10. Finally in the last stage, the relationships between these affecting factors and the landslide inventory map were calculated using Logistic regression algorithm. The amount of pseudo R2 (0.32) and AUC (0.85) shown the high efficiency of Logistic regression model. According to the coefficients obtained by the model, lithology is the most important factor affecting landslide occurrence (coefficient= +12.032). Most landslides (69%) are located within Ek Formation. The results indicated that 7.56% of the basin is located in high susceptibility class and 2.88% in very high susceptibility class. | ||
کلیدواژهها | ||
landslide؛ Logistic regression؛ Latyan catchment؛ PGA؛ Iran | ||
مراجع | ||
Atkinsson, P.M., R. Massari, 1998. Generalized linear modeling of susceptibility to landsliding in the central appennines, Italy. Computer & Geoscience, 24; 373- 385. Ayalew, L., H. Yamagishi, 2005. The application of GIS based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65; 15–31. Ayalew, L., H. Yamagishi, H. Marui, T. Kanno, 2005. Landslide in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparison of results from two methods and verifications. Enginering Geology, 81; 432–445. Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natur. Hazards and Earth System. Science, 5; 853–862. Bui, D.T., B. Pradhan, O. Lofman, I. Revhaug, O.B. Dick, 2011. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro- fuzzy inference system and GIS. Computer Geoscience, doi: 10.1016/ j.cageo.2011.10.031. Clark, W.A., P.L. Hosking, 1986. Statistical methods for Geographers. John Wily and Sons, New York. 518pp. Dai, F.C., C.F. Lee, 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42; 213–228. Dai, F.C., C.F. Lee, Z.W. Xu, 2001. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40; 381–391. Devkota, K.C., A.D. Regmi, H.R. Pourghasemi, 2013. 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. Natural Hazards, 65; 1-31. Dou, J., U. Paudel, T. Oguchi. 2015. Differentiation of shallow and deep-seated landslides using support vector machines: a case study of the Chuetsu area, Japan. Terr Atmos Ocean Sci., 26; 227–239. Duman, T.Y., T. Can, C. Gokceoglu, H. A. Nefeslioglu, H. Sonmez, 2006. Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51;241– 256. Ercanoglu, M., F.A. Temiz, 2011. Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environmental Earth Science, 64; 949–964. Formetta, G., G. Capparelli, P. Versace, 2016. Evaluating performance of simplified physically based models for shallow landslide susceptibility. Hydrol. Earth Syst. Sci., 20; 4585–4603. Garcia-Rodriguez, M.J., J.A. Malpica, B. Benito, M. Diaz, 2008. Susceptibility assessment of earthquaketriggered landslides in El Salvador using logistic regression. Geomorphology, 95;172–191. Gorsevski, P.V., P.E. Gessler, R.B. Foltz, W.J. Elliot, 2006. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS, 10; 395–415. Gritzner, M. L., W.A. Marcus, R. Aspinall, S.G. Custer, 2001. Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology, 37; 149–165. Iranian Landslide working party, 2007. Iranian landslides list, Forest, Rangeland and Watershed Association, Iran, 60p. Jaafari, A., A. Najafi, H.R. Pourghasemi, J. Rezaeian, A. Sattarian, 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int. J. Environ. Sci. Technol., 11; 909–926. Komac, M., 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology, 74;17–28. Lee, S., 2004. Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environmental Management, 34; 223-232. Lee, S., 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Senssing, 26;1477–1491. Lee, S., J. Choi, K. Min, 2004. Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing, 25; 2037–2052. Lee, S., K. Min, 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40;1095–1113. Lee, S., T. Sambath, 2006. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50; 847–855. Nefeslioglu, H.A., C. Gokceoglu, H. Sonmez, 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97; 171– 191. Oh, H.J., B. Pradhan, 2011. Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in tropical hilly area. Computer Geoscience, 37; 1264–1276. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, M. Mohammadi, H.R. Moradi, 2012a. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geoscience, doi: 10.1007/s12517-012-0532-7. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, K. Deylami Moezzi, 2012b. A comparative assessment of prediction capabilities of Dempster-Shafer and Weightsof-evidence models in landslide susceptibility mapping using GIS. Geomantic Natural Hazards Risk, doi: 10.1080/19475705.2012.662915. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, 2012c. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63; 965–996. Pourghasemi, H.R., B.M. Mohammady, B. Pradhan, 2012d. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97; 71–84. Pourghasemi, H.R., B.M. Mohammady, S.F. Aghda, 2013a. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural hazards, 69; 749–779. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, K.D. Moezzi, 2013b. A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk, 4;93–118. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, M. Mohammady, H.R. Moradi, 2013c. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences, 6; 2351–2365. Pourghasemi, H.R., M. Beheshtirad, B. Pradhan, 2014. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics Natural Hazards and Risk, http://dx.doi.org/10.1080/19475705.2014.984247
Pradhan, B., 2010a. Application of an advanced fuzzy logic model for landslide susceptibility analysis; International Journal of Computer and Intel Systems, 3; 370–381. Pradhan, B., 2010b. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Rem Sen, 38; 301–320. Regmi, A.D., K.C. Devkota, K. Yoshida, B. Pradhan, H.R. Pourghasemi, T. Kumamoto, A. Akgun, 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. Regmi, N.R., J.R. Giardino, J.D. Vitek, 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115; 172–187. Regmi, A.D., K. Yoshida, H. Nagata, A. Man, S. Pradhan, B. Pradhan, H.R. Pourghasemi, 2013. The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal Himalaya. Nat Hazards, 66; 501–532. Soto, J., J.P. Galve, J.A. Palenzuela, J.M. Azañón,
| ||
آمار تعداد مشاهده مقاله: 1,210 تعداد دریافت فایل اصل مقاله: 1,312 |