Spatial hazard analysis and prediction on rainfall-induced landslide using GIS
- 84 Downloads
The application of landslide hazard model coupled with GIS provides an effective means to spatial hazard analysis and prediction on rainfall-induced landslides. A modified SINMAP model is established based upon the systematic investigation on previous GIS-based landslide analysis models. By integrating the landslide deterministic model with the hydrological distribution model based on DEM, this model deeply studied the effect of underground water distribution due to rainfall on the slope stability and landslide occurrence, including the effect of dynamic water pressure resulting from the down slope seepage process as well as that of static water pressure. Its applicability has been testified on the Xiaojiang watershed, the rainfall-induced landslides widespread area in Southeast China. Detailed discussion was carried out on the spatial distribution characteristics of landslide hazard and its extending trend, as well as the quantitative relationship between landslide hazard with precipitation, slope angle and specific catchment area in the Xiaojiang watershed. And the precipitation threshold for landslide occurrence was estimated. These analytical results are proved useful for geohazard control and engineering decision-making in the Xiaojiang watershed.
Keywordsrainfall-induced landslide spatial analysis and prediction Geographical Information System
Unable to display preview. Download preview PDF.
- 1.Wen, B. P., The state of the art and trend of the landslide prediction, Earth Science Frontiers (in Chinese), 1996, 3(1–2): 86–92.Google Scholar
- 2.Yin, K. L., Yan, T. Z., Landslide prediction and related models, Chinese Journal of Rock Mechanics and Engieering (in Chinese), 1996, 15(1): 1–8.Google Scholar
- 3.Sun, G. Z., Yao, B. K., Landslide hazard in China and its research, (ed. Chinese Society of Rock Mechanics and Engineering) Typical landslides in China (in Chinese), Beijing: Science Press, 1988, 1–11.Google Scholar
- 10.Heckerman, Probabilistic interpretation of MYCIN’s certainty factors (eds. Kanal, L. N., Lemmer, J. E.), Uncertainty in Artificial Intelligence, New York: Elsevier, 1986, 298–311.Google Scholar
- 11.Chung, C. F., Fabbri, A. G., Probabilistic prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing (PE&RS), 1999, 65(12): 1388–1399.Google Scholar
- 15.Chung, C. F., Fabbri, A. G., Prediction models for landslide hazard using fuzzy set approach (eds. Marchetti, M., Rivas, V.), Geomorphology and Environmental Impact Assessment, Rotterdam: A. A. Balkema Publisher, 2001, 31–47.Google Scholar
- 18.Pack, R. T., Tarboton, D. G., Goodwin, C. N. The SINMAP approach to terrain stability mapping, Proceedings of 8th Congress of the International Assocation of Engineering Geology (eds. Moore, D., Hungr, O.), Rotterdam, A. A. Balema Publisher, 1998, 1157–1165.Google Scholar
- 19.Du, R. H., Kang, Z. C., Chen, X. Q. et al., Synthetic Investigation and control on the debris flow in the Xiaojiang watershed, Yunnan (in Chinese), Chongqing: Science Technology Press, 1987, 57–71.Google Scholar
- 20.Wu, J. S., Kang, Z. C., Tian, L. Q. et al., Observation on the Debris Flow in Jiangjiagou, Yunnan (in Chinese), Beijing: Science Press, 1990, 26–47.Google Scholar