JDR Vol.5 No.6 pp. 642-649
doi: 10.20965/jdr.2010.p0642


Neural Network-Based Risk Assessment of Artificial Fill Slope in Residential Urban Region

Toshitaka Kamai

Research Center of Landslides, Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan

August 9, 2010
September 29, 2010
December 1, 2010
hazard mapping, urban area, valley fill, neural network, earthquake
Destructive urban earthquakes have triggered landslides on gentle residential slopes in Japan. Earthquake-induced slope instability is closely related to artificial landforms, especially valley fill (embankments). The study of artificial landform changes has shown that differences in fill shape, such as depth, width, base angle inclination, and cross-sectional form, may be key discriminating factors in slope instability. The earthquake trigger mechanism must be considered in accurate estimation analysis, but it is difficult to include earthquake parameters in convenient linear multivariate analysis. Neural network analysis is applied to assess large fill slope instability in residential urban areas. The neural network model we developed including causative - fill shape, groundwater, and construction age - and triggering factors - distance from the fault, moment magnitude, and direction to the fault - was checked independently against another dataset and sensitivity was analyzed. Our proposed neural network model should enable us to establish more reliable landslide hazard mapping in residential urban areas, which, in turn, should aid disaster resilient societies in seismically active regions.
Cite this article as:
T. Kamai, “Neural Network-Based Risk Assessment of Artificial Fill Slope in Residential Urban Region,” J. Disaster Res., Vol.5 No.6, pp. 642-649, 2010.
Data files:
  1. [1] T. Kamai, Y. Kobayashi, C. Jinbo, and H. Shuzui, “Earthquake risk assessments of fill-slope instability in urban residential areas in Japan,” Landslides (Proc. 8th Int. Symp. Landslide), Thomas Telford, pp. 801-806, 2000.
  2. [2] T. Kamai, H. Shuzui, R. Kasahara, and Y. Kobayashi, “Earthquake risk assessments of large residential fill-slope in urban areas,” Landsides – Journal of the Japan Landslide Society, No.157, pp. 29-39, 2004 (in Japanese).
  3. [3] J. Freeman and D. Skapura, “Neural Networks Algorithms,” Applications and Programming Techniques, Addison-Wesley, first edition, pp. 89-128, 1991.
  4. [4] H. Sekiguchi, K. Irikura, and T. Iwata, “Fault geometry at the rupture termination of the 1995 Hyogo-ken Nanbu earthquake,” Bull. Seism. Soc. Am., Vol.90, pp. 117-133, 2000.
  5. [5] T. Usami, “Material for comprehensive list of destructive earthquales in Japan,” University of Tokyo Press, pp. 494, 1996 (in Japanese).
  6. [6] M. Kikuchi, “Realtime seismology,” University of Tokyo Press, pp. 222, 2003 (in Japanese).
  7. [7] T. Kamai and H. Shuzui, “Landslides in urban region,” Riko-tosho, pp. 200, 2002 (in Japanese).

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024