Skip to main content

Advertisement

Log in

Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region

  • Published:
Journal of Earth Science Aims and scope Submit manuscript

Abstract

Tens of thousands of landslides were triggered by May 12, 2008 earthquake over a broad area. The main purpose of this article is to apply and verify earthquake-triggered landslide hazard analysis techniques by using weight of evidence modeling in Qingshui (清水) River watershed, Deyang (德阳) City, Sichuan (四川) Province, China. Two thousand three hundred and twenty-one landslides were interpreted in the study area from aerial photographs and multi-source remote sensing imageries post-earthquake, verified by field surveys. The landslide inventory in the study area was established. A spatial database, including landslides and associated controlling parameters that may have influence on the occurrence of landslides, was constructed from topographic maps, geological maps, and enhanced thematic mapper (ETM+) remote sensing imageries. The factors that influence landslide occurrence, such as slope angle, aspect, curvature, elevation, flow accumulation, distance from drainages, and distance from roads were calculated from the topographic maps. Lithology, distance from seismogenic fault, distance from all faults, and distance from stratigraphic boundaries were derived from the geological maps. Normalized difference vegetation index (NDVI) was extracted from ETM+ images. Seismic intensity zoning was collected from Wenchuan (汶川) Ms8.0 Earthquake Intensity Distribution Map published by the China Earthquake Administration. Landslide hazard indices were calculated using the weight of evidence model, and landslide hazard maps were calculated from using different controlling parameters cases. The hazard map was compared with known landslide locations and verified. The success accuracy percentage of using all 13 controlling parameters was 71.82%. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low, and very low. The validation results showed satisfactory agreement between the hazard map and the existing landslides distribution data. The landslide hazard map can be used to identify and delineate unstable hazard-prone areas. It can also help planners to choose favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References Cited

  • Aleotti, P., Chowdhury, R., 1999. Landslide Hazard Assessment: Summary Review and New Perspectives. Bulletin of Engineering Geology and the Environment, 58(1): 21–44, doi:10.1007/s100640050066

    Article  Google Scholar 

  • Alexander, D. E., 2008. A Brief Survey of GIS in Mass-Movement Studies, with Reflections on Theory and Methods. Geomorphology, 94(3–4): 261–267, doi:10.1016/j.geomorph.2006.09.022

    Article  Google Scholar 

  • Anbalagan, R., 1992. Landslide Hazard Evaluation and Zonation Mapping in Mountainous Terrain. Engineering Geology, 32(4): 269–277, doi:10.1016/0013-7952 (92)90053-2

    Article  Google Scholar 

  • Arora, M. K., Das Gupta, A. S., Gupta, R. P., 2004. An Artificial Neural Network Approach for Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley, Himalayas. International Journal of Remote Sensing, 25(3): 559–572, doi:10.1080/0143116031000156819

    Article  Google Scholar 

  • Bai, S. B., Wang, J., Lu, G. N., et al., 2008. GIS-Based Landslide Susceptibility Mapping with Comparisons of Results from Machine Learning Methods Process versus Logistic Regression in Bailongjiang River Basin, China. Geophysical Research Abstracts, 10: EGU2008-A-06367

  • Bai, S. B., Wang, J., Lu, G. N., et al., 2009. GIS-Based and Data-Driven Bivariate Landslide-Susceptibility Mapping in the Three Gorges Area, China. Pedosphere, 19(1): 14–20, doi:10.1016/S1002-0160(08)60079-X

    Article  Google Scholar 

  • Begueria, S., Lorente, A., 2002. Landslide Hazard Mapping by Multivariate Statistics: Comparison of Methods and Case Study in the Spanish Pyrenees. http://en.scientificcommons.org/23860600

  • Bonham-Carter, G. F., 2002. Geographic Information Systems for Geoscientist: Modelling with GIS. In: Merriam, D. F., ed., Computer Methods in the Geosciences. Pergamon/Elsevier, New York. 302–334

    Google Scholar 

  • Brenning, A., 2005. Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. Natural Hazards and Earth System Sciences, 5(6): 853–862, doi:10.5194/nhess-5-853-2005

    Article  Google Scholar 

  • Caniani, D., Pascale, S., Sdao, F., et al., 2008. Neural Networks and Landslide Susceptibility: A Case Study of the Urban Area of Potenza. Natural Hazards, 45(1): 55–72, doi:10.1007/s11069-007-9169-3

    Article  Google Scholar 

  • Carrara, A., Cardinali, M., Guzzetti, F., et al., 1995. GIS Technology in Mapping Landslide Hazard. In: Carrara, A., Guzzetti, F., eds., Geographical Information Systems in Assessing Natural Hazards. Kluwer Academic Publisher, Dordrecht, the Netherlands. 135–175

    Google Scholar 

  • Carrara, A., Guzzetti, F., Cardinali, M., et al., 1999. Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard. Natural Hazards, 20(2–3): 117–135, doi:10.1023/A:1008097111310

    Article  Google Scholar 

  • Carrara, A., Pike, R. J., 2008. GIS Technology and Models for Assessing Landslide Hazard and Risk. Geomorphology, 94(3–4): 257–260, doi:10.1016/j.geomorph.2006.07.042

    Article  Google Scholar 

  • Chacon, J., Irigaray, C., Fernandez, T., et al., 2006. Engineering Geology Maps: Landslides and Geographical Information Systems. Bulletin of Engineering Geology and the Environment, 65(4): 341–411, doi:10.1007/s10064-006-0064-z

    Article  Google Scholar 

  • Chauhan, S., Sharma, M., Arora, M. K., et al., 2010. Landslide Susceptibility Zonation through Ratings Derived from Artificial Neural Network. International Journal of Applied Earth Observation and Geoinformation, 12(5): 340–350, doi:10.1016/j.jag.2010.04.006

    Article  Google Scholar 

  • Choi, J., Oh, H. J., Won, J. S., et al., 2010. Validation of an Artificial Neural Network Model for Landslide Susceptibility Mapping. Environmental Earth Sciences, 60(3): 473–483, doi:10.1007/s12665-009-0188-0

    Article  Google Scholar 

  • Chung, C. F., Fabbri, A. G., 1999. Probabilistic Prediction Models for Landslide Hazard Mapping. Photogrammetric Engineering and Remote Sensing, 65(12): 1389–1399

    Google Scholar 

  • Collison, A. J. C., Anderson, M. G., 1996. Using a Combined Slope Hydrology/Stability Model to Identify Suitable Conditions for Landslide Prevention by Vegetation in the Humid Tropics. Earth Surface Processes and Landforms, 21(8): 737–747, doi:10.1002/(SICI)1096-9837(199608)21:8〈737::AID-ES P674〉3.0.CO;2-F

    Article  Google Scholar 

  • Corominas, J., Moya, J., 2008. A Review of Assessing Landslide Frequency for Hazard Zoning Purposes. Engineering Geology, 102(3–4): 193–213, doi:10.1016/j.enggeo.2008.03.018

    Article  Google Scholar 

  • Dahal, R. K., Hasegawa, S., Nonoumra, A., et al., 2008a. Predictive Modelling of Rainfall-Induced Landslide Hazard in the Lesser Himalaya of Nepal Based on Weights-of-Evidence. Geomorphology, 102(3–4): 496–510, doi:10.1016/j.geomorph.2008.05.041

    Article  Google Scholar 

  • Dahal, R. K., Hasegawa, S., Nonomura, A., et al., 2008b. GIS-Based Weights-of-Evidence Modelling of Rainfall-Induced Landslides in Small Catchments for Landslide Susceptibility Mapping. Environmental Geology, 54(2): 311–324, doi:10.1007/s00254-007-0818-3

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., Li, J., et al., 2001. Assessment of Landslide Susceptibility on the Natural Terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3): 381–391, doi:10.1007/s002540000163

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., 2001. Terrain-Based Mapping of Landslide Susceptibility Using a Geographical Information System: A Case Study. Canadian Geotechnical Journal, 38(5): 911–923, doi:10.1139/cgj-38-5-911

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., 2002a. Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3–4): 213–228, doi:10.1016/S0169-555X(01)00087-3

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., 2002b. Landslides on Natural Terrain: Physical Characteristics and Susceptibility Mapping in Hong Kong. Mountain Research and Development, 22(1): 40–47, doi:http://dx.doi.org/10.1659/0276-4741(2002)022[0040:LONT]2.0.CO;2

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., 2003. A Spatiotemporal Probabilistic Modelling of Storm-Induced Shallow Landsliding Using Aerial Photographs and Logistic Regression. Earth Surface Processes and Landforms, 28(5): 527–545, doi:10.1002/esp.456

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., Ngai, Y. Y., 2002. Landslide Risk Assessment and Management: An Overview. Engineering Geology, 64(1): 65–87, doi:10.1016/S0013-7952(01)00093-X

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., Tham, L. G., et al., 2004. Logistic Regression Modelling of Storm-Induced Shallow Landsliding in Time and Space on Natural Terrain of Lantau Island, Hong Kong. Bulletin of Engineering Geology and the Environment, 63(4): 315–327, doi:10.1007/s10064-004-0245-6

    Article  Google Scholar 

  • Dai, F. C., Xu, C., Yao, X., et al., 2011. Spatial Distribution of Landslides Triggered by the 2008 Ms 8.0 Wenchuan Earthquake, China. Journal of Asian Earth Sciences, 40(4): 883–895, doi:10.1016/j.jseaes.2010.04.010

    Article  Google Scholar 

  • Dikau, R., Cavallin, A., Jager, S., 1996. Databases and GIS for Landslide Research in Europe. Geomorphology, 15(3–4): 227–239, doi:10.1016/0169-555X(95)00072-D

    Article  Google Scholar 

  • Ercanoglu, M., 2005. Landslide Susceptibility Assessment of SE Bartin (West Black Sea Region, Turkey) by Artificial Neural Networks. Natural Hazards and Earth System Sciences, 5(6): 979–992, doi:10.5194/nhess-5-979-2005

    Article  Google Scholar 

  • Gallus, D., Abecker, A., Richter, D., 2008. Classification of Landslide Susceptibility in the Development of Early Warning Systems. Symposium on Headway in Spatial Data Handling, Montpellier, France. 55–75

  • Garcia-Rodriguez, M. J., Malpica, J. A., Benito, B., et al., 2008. Susceptibility Assessment of Earthquake-Triggered Landslides in El Salvador Using Logistic Regression. Geomorphology, 95(3–4): 172–191, doi:10.1016/j.geomorph.2007.06.001

    Article  Google Scholar 

  • Godt, J. W., Baum, R. L., Savage, W. Z., et al., 2008. Transient Deterministic Shallow Landslide Modeling: Requirements for Susceptibility and Hazard Assessments in a GIS Framework. Engineering Geology, 102(3–4): 214–226, doi:10.1016/j.enggeo.2008.03.019

    Article  Google Scholar 

  • Gunther, A., Thiel, C., 2009. Combined Rock Slope Stability and Shallow Landslide Susceptibility Assessment of the Jasmund Cliff Area (Rügen Island, Germany). Natural Hazards and Earth System Sciences, 9(3): 687–698, doi:10.5194/nhess-10-2197-2010

    Article  Google Scholar 

  • Guzzetti, F., Carrara, A., Cardinali, M., et al., 1999. Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy. Geomorphology, 31(1–4): 181–216, doi:10.1016/S0169-555X(99)00078-1

    Article  Google Scholar 

  • Guzzetti, F., 2003. Landslide Hazard Assessment and Risk Evaluation: Limits and Prospective. In: Proceedings of the 4th EGS Plinius Conference Held at Mallorca, Spain, October 2002. 4

  • Hasegawa, S., Dahal, R. K., Nishimura, T., et al., 2009. DEM-Based Analysis of Earthquake-Induced Shallow Landslide Susceptibility. Geotechnical and Geological Engineering, 27(3): 419–430, doi:10.1007/s10706-008-9242-z

    Article  Google Scholar 

  • Havenith, H. B., Strom, A., Caceres, F., et al., 2006. Analysis of Landslide Susceptibility in the Suusamyr Region, Tien Shan: Statistical and Geotechnical Approach. Landslides, 3(1): 39–50, doi:10.1007/s10346-005-0005-0

    Article  Google Scholar 

  • He, Y. P., Beighley, R. E., 2008. GIS-Based Regional Landslide Susceptibility Mapping: A Case Study in Southern California. Earth Surface Processes and Landforms, 33(3): 380–393, doi:10.1002/esp.1562

    Article  Google Scholar 

  • Jadda, M., Shafri, H. Z. M., Mansor, S. B., et al., 2009. Landslide Susceptibility Evaluation and Factor Effect Analysis Using Probabilistic-Frequency Ratio Model. European Journal of Scientific Research, 33(4): 654–668

    Google Scholar 

  • Kamp, U., Growley, B. J., Khattak, G. A., et al., 2008. GIS-Based Landslide Susceptibility Mapping for the 2005 Kashmir Earthquake Region. Geomorphology, 101(4): 631–642, doi:10.1016/j.geomorph.2008.03.003

    Article  Google Scholar 

  • Keefer, D. K., 1984. Landslides Caused by Earthquakes. Geological Society of America Bulletin, 95(4): 406–421, doi:10.1130/0016-7606(1984)95〈406:LCBE〉2.0.CO;2

    Article  Google Scholar 

  • Keefer, D. K., Larsen, M. C., 2007. Assessing Landslide Hazards. Science, 316(5828): 1136–1138, doi:10.1126/science.1143308

    Article  Google Scholar 

  • Kouli, M., Loupasakis, C., Soupios, P., et al., 2010. Landslide Hazard Zonation in High Risk Areas of Rethymno Prefecture, Crete Island, Greece. Natural Hazards, 52(3): 599–621, doi:10.1007/s11069-009-9403-2

    Article  Google Scholar 

  • Lee, S., 2004. Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS. Environmental Management, 34(2): 223–232, doi:10.1007/s00267-003-0077-3

    Article  Google Scholar 

  • Lee, S., Choi, J., 2004. Landslide Susceptibility Mapping Using GIS and the Weight-of-Evidence Model. International Journal of Geographical Information Science, 18(8): 789–814, doi:10.1080/13658810410001702003

    Article  Google Scholar 

  • Lee, S., Sambath, T., 2006. Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Uusing Frequency Ratio and Logistic Regression Models. Environmental Geology, 50(6): 847–855, doi:10.1007/s00254-006-0256-7

    Article  Google Scholar 

  • Lee, S., Evangelista, D. G., 2006. Earthquake-Induced Landslide-Susceptibility Mapping Using an Artificial Neural Network. Natural Hazards and Earth System Sciences, 6(5): 687–695, doi:10.5194/nhess-6-687-2006

    Article  Google Scholar 

  • Lee, C. T., 2006. Methodology for Estimation of Earthquake-Induced Landslide Probability and Result Evaluation. Geophysical Research Abstracts, 8: 05759

    Google Scholar 

  • Lee, C. T., Huang, C. C., Lee, J. F., et al., 2008. Statistical Approach to Earthquake-Induced Landslide Susceptibility. Engineering Geology, 100(1–2): 43–58, doi:10.1016/j.enggeo.2008.03.004

    Article  Google Scholar 

  • Lin, M. L., Tung, C. C., 2003. A GIS-Based Potential Analysis of the Landslides Induced by the Chi-Chi Earthquake. Engineering Geology, 71(1–2): 63–77, doi:10.1016/S0013-7952(03)00126-1

    Google Scholar 

  • Luzi, L., Pergalani, F., 1999. Slope Instability in Static and Dynamic Conditions for Urban Planning: The ‘Oltre Po Pavese’ Case History (Regione Lombardia-Italy). Natural Hazards, 20(1): 57–82, doi:10.1023/A:1008162814578

    Article  Google Scholar 

  • Magliulo, P., Lisio, A. D., Russo, F., et al., 2008. Geomorphology and Landslide Susceptibility Assessment Using GIS and Bivariate Statistics: A Case Study in Southern Italy. Natural Hazards, 47(3): 411–435, doi:10.1007/s11069-008-9230-x

    Article  Google Scholar 

  • Magliulo, P., Lisio, A. D., Russo, F., 2009. Comparison of GIS-Based Methodologies for the Landslide Susceptibility Assessment. Geoinformatica, 13(3): 253–265, doi:10.1007/s10707-008-0063-2

    Article  Google Scholar 

  • Mavrouli, O., Corominas, J., Wartman, J., 2009. Methodology to Evaluate Rock Slope Stability under Seismic Conditions at Solà de Santa Coloma, Andorra. Natural Hazards and Earth System Sciences, 9(6): 1763–1773, doi:10.5194/nhess-9-1763-2009

    Article  Google Scholar 

  • Miles, S. B., Ho, C. L., 1999. Rigorous Landslide Hazard Zonation Using Newmark’s Method and Stochastic Ground Motion Simulation. Soil Dynamics and Earthquake Engineering, 18(4): 305–323, doi:10.1016/S0267-7261(98)00048-7

    Article  Google Scholar 

  • Mora, S., Vahrson, W., 1994. Macrozonation Methodology for Landslide Hazard Determination. Bulletin of Association of Engineering Geologists, 31(1): 49–58

    Google Scholar 

  • Oh, H. J., Lee, S., 2011. Landslide Susceptibility Mapping on Panaon Island, Philippines Using a Geographic Information System. Environmental Earth Sciences, 62(5): 935–951, doi:10.1007/s12665-010-0579-2

    Article  Google Scholar 

  • Pachauri, A. K., Gupta, P. V., Chander, R., 1998. Landslide Zoning in a Part of the Garhwal Himalayas. Environmental Geology, 36(3–4): 325–334, doi:10.1007/s002540050348

    Article  Google Scholar 

  • Pandey, A., Dabral, P. P., Chowdary, V. M., et al., 2008. Landslide Hazard Zonation Using Remote Sensing and GIS: A Case Study of Dikrong River Basin, Arunachal Pradesh, India. Environmental Geology, 54(7): 1517–1529, doi:10.1007/s00254-007-0933-1

    Article  Google Scholar 

  • Pareek, N., Sharma, M. L., Arora, M. K., 2010. Impact of Seismic Factors on Landslide Susceptibility Zonation: A Case Study in Part of Indian Himalayas. Landslides, 7(2): 191–201, doi:10.1007/s10346-009-0192-1

    Article  Google Scholar 

  • Patwary, M. A. A., Champati Ray, P. K., Parvaiz, I., 2009. IRS-LISS-III and PAN Data Analysis for Landslide Susceptibility Mapping Using Heuristic Approach in Active Tectonic Region of Himalaya. Journal of the Indian Society of Remote Sensing, 37(7): 493–509, doi:10.1007/s12524-009-0036-4

    Article  Google Scholar 

  • Pradhan, B., Lee, S., 2008. Utilization of Optical Remote Sensing Data and GIS Tools for Regional Landslide Hazard Analysis Using an Artificial Neural Network Model. Earth Science Frontiers, 14(6): 143–152, doi:10.1016/S1872-5791(08)60008-1

    Google Scholar 

  • Pradhan, B., Lee, S., 2010a. Regional Landslide Susceptibility Analysis Using Back-Propagation Neural Network Model at Cameron Highland, Malaysia. Landslides, 7(1): 13–30, doi:10.1007/s10346-009-0183-2

    Article  Google Scholar 

  • Pradhan, B., Lee, S., 2010b. Landslide Susceptibility Assessment and Factor Effect Analysis: Backpropagation Artificial Neural Networks and Their Comparison with Frequency Ratio and Bivariate Logistic Regression Modelling. Environmental Modelling and Software, 25(6): 747–759, doi:10.1016/j.envsoft.2009.10.016

    Article  Google Scholar 

  • Pradhan, B., Lee, S., 2010c. Delineation of Landslide Hazard Areas on Penang Island, Malaysia, by Using Frequency Ratio, Logistic Regression, and Artificial Neural Network Models. Environmental Earth Sciences, 60(5): 1037–1054, doi:10.1007/s12665-009-0245-8, doi:10.1007/s12665-009-0245-8

    Article  Google Scholar 

  • Pradhan, B., Singh, R. P., Buchroithner, M. F., 2006. Estimation of Stress and Its Use in Evaluation of Landslide Prone Regions Using Remote Sensing Data. Advances in Space Research, 37(4): 698–709, doi:10.1016/j.asr.2005.03.137

    Article  Google Scholar 

  • Pradhan, B., Youssef, A. M., Varathrajoo, R., 2010. Approaches for Delineating Landslide Hazard Areas Using Different Training Sites in an Advanced Atificial Neural Network Model. Geo-Spatial Information Science, 13(2): 93–102, doi:10.1007/s11806-010-0236-7

    Article  Google Scholar 

  • Saha, A. K., Gupta, R. P., Sarkar, I., et al., 2005. An Approach for GIS-Based Statistical Landslide Susceptibility Zonation—With a Case Study in the Himalayas. Landslides, 2(1): 61–69, doi:10.1007/s10346-004-0039-8

    Article  Google Scholar 

  • Sassa, K., Tsuchiya, S., Ugai, K., et al., 2009. Landslides: A Review of Achievements in the First 5 Years (2004–2009). Landslides, 6(4): 275–286, doi:10.1007/s10346-009-0172-5

    Article  Google Scholar 

  • Shaban, A., Khawlie, M., Kheir, R. B., et al., 2001. Assessment of Road Instability along a Typical Mountainous Road Using GIS and Aerial Photos, Lebanon-Eastern Mediterranean. Bulletin of Engineering Geology and the Environment, 60(2): 93–101, doi:10.1007/s100640000092

    Article  Google Scholar 

  • Singh, L. P., van Westen, C. J., Champati Ray, P. K., et al., 2005. Accuracy Assessment of InSAR Derived Input Maps for Landslide Susceptibility Analysis: A Case Study from the Swiss Alps. Landslides, 2(3): 221–228, doi:10.1007/ s10346-005-0059-z

    Article  Google Scholar 

  • Temesgen, B., Mohammed, M. U., Korme, T., 2001. Natural Hazard Assessment Using GIS and Remote Sensing Methods, with Particular Reference to the Landslides in the Wondogenet Area, Ethiopia. Physics and Chemistry of the Earth, Part C, 26(9): 665–675, doi:10.1016/S1464-1917(01)00065-4

    Google Scholar 

  • van Westen, C. J., 2004. Geo-Information Tools for Landslide Risk Assessment: An Overview of Recent Developments. In: Lacerda, W. A., Ehrlich, M., Fontoura, S. A. B., et al., eds., Landslides: Evaluation and Stabilization—Glissement de Terrain: Evaluation et Stabilisation: Proceedings of the 9th International Symposium on Landslides. Rio de Janeiro, Brazil. 39–56

  • van Westen, C. J., Castellanos, E., Kuriakose, S. L., 2008. Spatial Data for Landslide Susceptibility, Hazard, and Vulnerability Assessment: An Overview. Engineering Geology, 102(3–4): 112–131, doi:10.1016/j.enggeo.2008.03.010

    Google Scholar 

  • van Westen, C. J., Rengers, N., Soeters, R., 2003. Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment. Natural Hazards, 30(3): 399–419, doi:10.1023/B:NHAZ.0000007097.42735.9e

    Article  Google Scholar 

  • van Westen, C. J., van Asch, T. W. J., Soeters, R., 2006. Landslide Hazard and Risk Zonation—Why Is It still so Difficult? Bulletin of Engineering Geology and the Environment, 65(2): 167–184, doi:10.1007/s10064-005-0023-0

    Article  Google Scholar 

  • Wang, H. B., Liu, G. J., Xu, W. Y., et al., 2005. GIS-Based Landslide Hazard Assessment: An Overview. Progress in Physical Geography, 29(4): 548–567, doi:10.1191/0309133305pp462ra

    Article  Google Scholar 

  • Wang, H. B., Sassa, K., 2006. Rainfall-Induced Landslide Hazard Assessment Using Artificial Neural Networks. Earth Surface Processes and Landforms, 31(2): 235–247, doi:10.1002/esp.1236

    Article  Google Scholar 

  • Wu, S. R., Jin, Y. M., Zhang, Y. S., et al., 2004. Investigations and Assessment of the Landslide Hazards of Fengdu County in the Reservoir Region of the Three Gorges Project on the Yangtze River. Environmental Geology, 45(4): 560–566, doi:10.1007/s00254-003-0911-1

    Article  Google Scholar 

  • Xu, X. W., Wen, X. Z., Yu, G. H., et al., 2009a. Coseismic Rreverse- and Oblique-Slip Surface Faulting Generated by the 2008 Mw 7.9 Wenchuan Earthquake, China. Geology, 37(6): 515–518, doi:10.1130/G25462A.1

    Article  Google Scholar 

  • Xu, X. W., Yu, G. H., Chen, G. H., et al., 2009b. Parameters of Coseismic Reverse- and Oblique-Slip Surface Ruptures of the 2008 Wenchuan Earthquake, Eastern Tibetan Plateau. Acta Geologica Sinica, 83(4): 673–684, doi:10.1111/j.1755-6724.2009.00091.x

    Article  Google Scholar 

  • Xu, C., Dai, F. C., Chen, J., et al., 2009c. Identification and Analysis of Secondary Geological Hazards Triggered by a Magnitude 8.0 Wenchuan Earthquake. Journal of Remote Sensing, 13(4): 745–762 (in Chinese with English Abstract)

    Google Scholar 

  • Yao, X., Dai, F. C., 2006. Support Vector Machine Modeling of Landslide Susceptibility Using a GIS: A Case Study. IAEG 2006, 793

    Google Scholar 

  • Yao, X., Tham, L. G., Dai, F. C., 2008. Landslide Susceptibility Mapping Based on Support Vector Machine: A Case Study on Natural Slopes of Hong Kong, China. Geomorphology, 101(4): 572–582, doi:10.1016/j.geomorph.2008.02.011

    Article  Google Scholar 

  • Yilmaz, I., 2009a. Landslide Susceptibility Mapping Using Frequency Ratio, Logistic Regression, Artificial Neural Networks and Their Comparison: A Case Study from Kat Landslides (Tokat-Turkey). Computers and Geosciences, 35(6): 1125–1138, doi:10.1016/j.cageo.2008.08.007

    Article  Google Scholar 

  • Yilmaz, I., 2009b. A Case Study from Koyulhisar (Sivas-Turkey) for Landslide Susceptibility Mapping by Artificial Neural Networks. Bulletin of Engineering Geology and the Environment, 68(3): 297–306, doi:10.1007/s10064-009-0185-2

    Article  Google Scholar 

  • 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(4): 821–836, doi:10.1007/s12665-009-0394-9

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiwei Xu  (徐锡伟).

Additional information

This study was supported by the International Scientific Joint Project of China (No. 2009DFA21280), the National Natural Science Foundation of China (No. 40821160550), and the Doctoral Candidate Innovation Research Support Program by Science & Technology Review (No. kjdb200902-5).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, C., Xu, X., Dai, F. et al. Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. J. Earth Sci. 23, 97–120 (2012). https://doi.org/10.1007/s12583-012-0236-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12583-012-0236-7

Key Words

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy