نشریه علمی - پژوهشی مرتع و آبخیزداری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده منابع طبیعی دانشگاه تربیت مدرس، دکتری جغرافیای طبیعی

2 دانشگاه تربیت مدرس

3 دانشگاه ملایر

چکیده

 
هدف از این تحقیق پهنه‌بندی خطر زمین‌لغزش در حوضة آبخیز سیاه‌دره با استفاده از مدل آماری رگرسیون لجستیک است. بدین منظور، نخست نقاط لغزشی با استفاده از عکس‌های هوایی و بازدید‌های میدانی مشخص و متعاقب آن نقشة پراکنش زمین‌لغزش منطقه تهیه شد. سپس، هر یک از عوامل مؤثر در وقوع زمین‌لغزش در منطقة مورد مطالعه از قبیل شیب، جهت شیب، ارتفاع، لیتولوژی، کاربری اراضی، فاصله از جاده، فاصله از شبکة آبراهه، فاصله از گسل، و نقشة همباران در محیط GIS تهیه شد. داده‌های مذکور در فرمت‌های برداری و رستری در سامانة اطلاعات جغرافیایی ذخیره شد و برای اعمال تحلیل‌های مبتنی بر مدل لجستیک استفاده گردید. تجزیه و تحلیل لجستیک با استفاده از نرم‌افزارARC GIS 9.2  و SPSS صورت گرفت. نتایج نشان داد عوامل شیب، ارتفاع، بارندگی، فاصله از آبراهه، و فاصله از گسل به‌ترتیب مهم‌ترین عوامل وقوع لغزش در منطقه‌اند. بیشتر لغزش‏های منطقه در کلاس‏های شیب 22/22 تا 33/33 درصد، ارتفاع 2350 ـ 2500 متر، و بارندگی 473 ـ 523 میلی‌متر قرار گرفته است. 50 درصد زمین‌لغزش‌ها در فاصلة 30 متری از آبراهه قرار گرفته است. در این منطقه، اکثر لغزش‏ها تا فاصلة 300 متری از گسل‏ها اتفاق افتاده‏اند. صحتِ مدل با سه روش ارزیابی شد. نتایج به‌دست‌آمده از هر سه روش برای حضور همة متغیر‌ها به‌ترتیب عبارت است از: 2/98 درصد، 692/0، و 519/0. بدین ترتیب، نشان داده شد که میزان دقت مدل آماری لجستیک در تهیة نقشة حساسیت به خطر لغزش در منطقة مورد مطالعه بسیار بالاست.
 

کلیدواژه‌ها

عنوان مقاله [English]

Land slide hazard Susceptibility Mapping and its Evaluation Using the Statistics Analysis logistic Regression

نویسنده [English]

  • Hamid Reza Moradi 1

1

2

3

چکیده [English]

ABSTRACT
Aim Of this research is landslide hazard zoning in Syahdare watershed using logistic regression. Therefore, outset landslide points recognized using air photography and extensive field studies. Then distribution of landslide map was makes. Then each effective element on landslide occurred for example slope, aspect, elevation, litho logy, land use, distance of road, distance of drainage, distance of fault and precipitation map makes in GIS environment. These data were saved in raster and vector format in GIS soft ware and they used for analysis with logistic regression. Logistic analysis obtained by Arc GIS 9.2 soft ware and SPSS. Results showed the most important elements in Land slide occurred in this area are slope, elevation, precipitation, distance of drainage and distance of fault respectively. Most of the land slides have occurred in the classes of 10 to 15 degree slope, elevation of 2350-2500 meters, precipitation (473-523 mm) are located. 50% Landslide is located at a distance of 30 meters of the stream. In this region the most landslides are occurrence in the 300 meter to fault distance. While the from 500 meter distance to the fault reduced number and susceptibility to landslides. The evaluation of accuracy model and the results obtained with three methods for the presence of all variables, 98.2 percent, 0.692 and 0.519 respectively. So showed that logistic regression had high accuracy in making landslide susceptibility map in study area.

کلیدواژه‌ها [English]

  • landslide
  • Logistic regression
  • Geographical Information System
  • Syahdare watershed
[1] Ayalew, L. and Ymagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakud-Yahiko Mountains, Central Japan, The journal of Geomorphology,65, 15-31.
[2] Bilifard, F., Jaboyedoff, M. and Satori, M. (2003). Rock fall hazard mapping a long a mountainous road in Switzerland using a GIS-based parameter rating approach, Natural Hazard and Earth System Sciences, 3, 431-438.
[3] Can, T., Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H. and Duman, Y. (2005). Susceptibility assessments of shallow earth flows triggered by heavy rainfall at three catchment's by logistic regression analysis, The journal of Geomorphology, 82, 250-271.
[4] Chau, K.T. and Chan, J.E. (2005). Regional bias of landslide data in generating susceptibility maps using logistic regression for Hong Kong Island (2005). Rock Mechanic, 41(2): 280-290.
[5] Chau, K.T., Tang, Y.F. and Wong, R.H.C. (2004). GIS-Based Rock fall hazard map for Hong Kong. Rock Mechanic, 41(3), 1-6.
[6] Chau, K.T., Sze, Y.L., Fung, M.K., Wong, W.Y., Fong, E.L. and Chan, L.C.P. (2004). Landslide hazard analysis for hong kong using landslide inventory and GIS, Computers & Geosciences, 30, 429-443.
[7] Chen, Z. and Wang, J. (2007). Land slide hazard mapping using logistic rsgression model in Mskenzie Vally , Canada , Natural Hazards, 42, 75-89.
[8] Dai, F.C., Lee, F.C., Tham, L.G., Ng, K.C. and Shum, W.L. (2004). Logistic regression modeling of stoem-indused shallow land sliding in time and space on lantau island, Hong Kong, Bullten Engineering Geology Environment,63, 315-327.
[9] Dai, F.C. and Lee, C.F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213-228.
[10] Dashti marvili, M. (2008). Land slide Hazard Zoning Using Logistic Regression(Case Study :A Part Of Gamasiab Watershed), M.Sc, Thesis in Watershed Management Engineering, College Of Natural resources and marine Science, 73p.
[11] Duman, T.Y., Can, T., Gokceoglu, C., Nefeslioglu, A.H. and Sonmez, H. (2006). Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey, Environ mental Geology, 51, 241-256.
[12] Dominguez-Cuesta, M., Jimenez-Sonchez, M. and Berrezueta, E. (2007). Landslide UN the central coalfield (Cantabarian Mountains, NW Spain): Geomorphologic feature conditioning factors and meteorological implication in susceptibility assessment, Geomorphology, 89, 1-12, Geomorphology, 43, 117-136.
[13] Enrique, A., Castellanos Abella, A., Cees, J. and Van Weston, B. (2008). Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba, The journal of Geomorphology, 94, 453-466.
[14] Garcia-Rodriguez, M.J., Malpica, J.A., Benito, B. and Diaz, M. (2008). Suseptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression, Geomorphology, 95, 172-191.
[15] Greco, R., Sorriso, Valvo, and Catalano, E. (2007). Logistic regression analysis in the evaluation of mass movement's susceptibility case study: Calabria, Italy, The journal of Engineering Geology, 89, 47-66.
[16] Iswar, D., Sashikant, S., Cees, V.W., Alfred, S. and Robert, H. (2010). Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas, India, Geomorphology, 114, 627-637.
[17] Karam, E. (2004). Application WLC model in Landslide occurrence potential zonation, (case study: Sorkhon area in Charmahal O Bakhtyari province, Journal Geography and Development, pp. 131-141.
[18] Khamechian, M., Abdolmaleki, P. and Rakei, B. (2005). Application logistic regression for Landslide hazard zonation in Sepidar Galeh area of Semnan Province, Journal Technical science Amir Kabir, 62(16): 65-76.
[19] Lamelas, M.T., Marinoni, O., Hoppe, A. and Riva, J. (2008). Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain), Environmental Geology, 54, 63-977.
[20] Lee, S. and Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models, Environ. Geol, 50, 847-855.
[21] Lee, S. (2004). Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS, Environmental Management, 34, 223-232.
[22] Lee, S. and Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models, Landslides, 4,33-41.
[23] Mosavi, Z. (2008). Modeling and Landslide occurrence zonation use logistic multi regression (case study: Sajaroud watershed), M.Sc, thesis in watershed management, Natural resources faculty Mazandran university, 115 p.
[24] Mosavi, Z., Kavian, A. and Soleimani, K. (2010). Landslide Susceptibility Mapping in Sajaroud Basin Using logistic Regression Model, Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 14)33): 99-111.
[25]Moradi, H.R., Dashti marvili, M. and Ildoromi, A. (2013). Land slide hazard Susceptibility Mapping and its Evaluation Using the Statistics Analysis logistic Regression, Iranian Journal of Watershed Management Science and Engineering, 6(19): 67 -70.
[26] Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the prepration of landslide susceptibility maps, Engineering Geology, 97, 171-191.
[27] Oh, H.J., Saro, L. and Wisut, Ch. (2009). Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand, Environmental Geology, 57, 641-651.
[28] Ohlamcher, G.C. and Davis, J.C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas USA, Engineering Geology, 69, 331-343.
[29] Pradhan, B. and Lee, S. (2009). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models, Environmental Erath Science, 60(5): 1037-1054.
[30] Shadfar, S., Yamani, M., and Namaki, M. (2005). Landslide hazard zonation using information value, surface density and LNRE models in Chalakroud watershed, Journal of Water and Watershed, 3, 62-68.
[31] Shirzadi, A., Solaimani, K., Habibnejhad, M. and Mousavi, R. (2006). Rock fall hazard susceptibility mapping by a statistical- logistic regression model (case study: Kurdistan, Salavatabad saddle), 3rd National Watershed Water and Soil Resources Management Conference, Kerman, Iran, 514-518.
[32] Yaclin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations, Catena, 72,1-12.
[33] Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial Neural network sand their comparison: A case study from Kat landslides (Tokat-Turkey), Computers & Geosciences, 1-14.