World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012)

By Li, Y.

Click here to view

Book Id: WPLBN0004017952
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012)  
Author: Li, Y.
Volume: Vol. 12, Issue 8
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Zhou, G., Tang, C., Zheng, L., Li, Y., & Chen, G. (2012). Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012). Retrieved from http://worldlibrary.net/


Description
Description: Department of Civil and Structural Engineering, Kyushu University, Fukuoka, Japan. A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps.

Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%.


Summary
Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Excerpt
Soeters, R. and van Westen, C. J.: Slope stability recognition, analysis, and zonation application of geographical information system to landslide hazard zonation, in: Landslides: Investigation and Mitigation. Sp.-Rep., edited by: Turner, A. K. and Schuster, R. L., 247, Transportation Research Board, National Research Council. National Academy Press, Washington, DC, 129–177, 1996.; Tretkoff, E.: Calculating specific catchment area, Eos Trans. AGU, 92, 232, doi:10.1029/2011EO270019, 2011.; van Westen, C. J., van Asch, T. W. J., and Soeters, R.: Landslide hazard and risk zonation-why is it still so difficult?, Bull. Eng. Geol. Environ., 65, 167–184, 2006.; van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng. Geol. 102, 112–131, 2008.; Wieczorek, G. F.: Preparing a detailed landslide-inventory map for hazard evaluation and reduction, Bull. Assoc. Eng. Geol., 21, 337–342, 1984.; Xie, M. W., Esaki, T., and Zhou, G. Y.: GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model, Nat. Hazards, 33, 265–282, 2004.; Ayalew, L., Kasahara, M., and Yamagishi, H.: The spatial correlation between earthquakes and landslides in Hokkaido (Japan), a GIS-based analysis of the past and the future, Landslides, doi:10.1007/s10346-011-0262-z, 2011.; Bai, S. B., Wang, J., Lu, G. N., Zhou, P. G., Hou, S. S., and Xu, S. N.: GIS-based logistic regression for landslide susceptibility apping of the Zhongxian segment in the Three Gorges area, China, Geomorphology 115, 23–31, 2010.; Can, T., Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., and Duman, T. Y.: Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses, Geomorphology 72, 250–271, 2005.; Carrara, A. and Guzzetti, F.: Geographical Information Systems in Assessing Natural Hazards. Kluwer Academic Publishers, Dordrecht The Netherlands, 353, 1995.; Chang, K. T., Chiang, S. H., and Hsu, M. L.: Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression, Geomorphology, 89, 335–347, 2007.; Chung, C. F. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Remote Sens., 65, 1389–1399, 1999.; Conoscenti, C., Maggio, C. D. and Rotigliano, E.: GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy), Geomorphology, 94, 325–339, 2008.; Xu, Q., Zhang, S., and Li, W. L.: Spatial Distribution of Large-scale Landslides Induced by the 5.12 Wenchuan Earthquake, Science Press and Institute of Mountain Hazards and Environment, J. Mt. Sci. 8, 246–260, doi:10.1007/s11629-011-2105-8, 2011.; Crozier, M. J.: Multiple-occurrence regional landslide events in New Zealand: Hazard management issues, Landslides, 2, 247–256, doi:10.1007/s10346-005-0019-7, 2005.; Dahal, R. K. and Hasegawa, S.: Representative rainfall thresholds for landslides in the Nepal Himalaya, Geomorphology, 100, 429–443, 2008.; Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228, 2002.; Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005.; Garrett, J.: Where and why artificial neural networks are applicable in civil engineering, J. Comput. Civil Eng., 8, 129–130, 1994.; Glade, T., Crozier, M. J., and Smith, P.: Applying Probability Determination to Refine Landslide-triggering Rainfall Thresholds Using an Empirical Antecedent Daily Rainfall Model, Pure Appl.

 

Click To View

Additional Books


  • Landslide Early Warning Based on Failure... (by )
  • Integration of Theoretical and the Empir... (by )
  • Impacts of Anthropogenic and Environment... (by )
  • Rn and Co2 Geochemistry of Soil Gas Acro... (by )
  • Forecasting Severe Ice Storms Using Nume... (by )
  • Analysis of the Local Lithospheric Magne... (by )
  • Sensibility Analysis of Voris Lava-flow ... (by )
  • Hiresss: a Physically Based Slope Stabil... (by )
  • Applications of Simulation Technique on ... (by )
  • Can an Early Warning System Help Minimiz... (by )
  • Residential Building and Occupant Vulner... (by )
  • The Pulse Azimuth Effect as Seen in Indu... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from World Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.