Abstract
Debris flow susceptibility assessment is the premise of risk assessment. In this paper, Sichuan Province is chosen as a study area, where debris flow disasters happen frequently. Information value model is applied to calculate the information values of seven environmental factors, namely elevation, slope, aspect, flow accumulation, vegetation coverage, soil type and land-use type. Geographic information system technology is used to analyze the comprehensive information values so as to determine the debris flow susceptibility. The results show that the northeast, the central and the south of Sichuan are the most hazardous regions, which display a zonal distribution feature from the southeast to the south. From the validation results, 7.53 % of the total area suffers from high susceptibility and 19.97 % suffers from very high susceptibility. However, 80 % of the debris flows are concentrated in two regions. The actual occurrence ratios of debris flows of the high-susceptibility and very high-susceptibility areas are 4.95 and 2.14, respectively.
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References
Carrara A, Crosta G, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378
Dai FC, Lee CF (2002) Landslide characteristics and, slope instability modeling using GIS, Lantau Island. Hong Kong. Geomorphology 42(3–4):213–228
Lan HX, Wu FQ, Wang SJ (2002a) Landslide CF multi-variable regression model and its application. J Mt Sci 20(6):732–737
Lan HX, Wu FQ, Zhou CH (2002b) Analysis on susceptibility of GIS based landslide triggering factors in Yunnan Xiaojiang watershed. Chin J Rock Mech Eng 21(10):1500–1506
Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manage 34(2):223–232
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin. Korea. Environ Geol 40(9):1095–1113
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41
Ma J (ed) (2002) Modern physical geography. Beijing Normal University, Beijing
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas. USA. Eng Geol 69(3–4):331–343
Prabu S, Ramakrishnan SS (2009) Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN. J Indian Soc Remot 37(3):409–421
Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493
Prandhan B, Oh HJ, Buchroithner M (2010) Weight-of-evidence model applied to landslide susceptibility mapping in a tropical hill area. Geomatics. Nat Hazards Risk 1(3):199–223
Ruan SY, Huang RQ (2001) Application of GIS-based information model on assessment of geological hazards risk. J Chengdu Univ Technol 28(1):89–92
Soeters R, van Westen CJ (1996) Slope instability recognition, analysis, and zonation. Landslides, investigation and mitigation. In: Turner AK, Schuster RL (eds) Transportation Research Board, National Research Council, special report. National Academy Press, Washington, DC, pp 129–177
van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131
Wang SY, Liu JS, Yang CJ (2008) Eco-environmental vulnerability evaluation in the Yellow River Basin. China. Pedosphere 18(2):171–182
Xu WB, Yu WJ, Zhang GP (2012) Prediction method of debris flow by logistic model with two types of rainfall: a case study in the Sichuan. China. Nat Hazards 62(2):733–744
Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266
Zhang GP, Xu J, Bi BG (2009) Relations of landslide and debris flow hazards to environment factors. Chin J Appl Ecol 20(3):653–658
Zhu HB, Mu HD, Wang JZ (2003) Application of analytic hierarchy process on zoning hazard degree of geologic disaster in Taihang Mountain region. Chin J Geol Hazard Control 13(3):125–129
Zhu LF, Xu XC, Yin KL (2004) Risk zonation of landslide in China based on information content model. J Earth Sci Environ 26(3):52–56
Acknowledgments
This work is supported by the National Natural Science Foundation of China (40971016), National Science and Technology Support Program (2012BAK19B04-03) and the Fundamental Research Funds for the Central Universities (103.1.2 E022050205A03007023401094; 103.1.2 E022050205A03008023401021; 103.1.2 E022050205A030080023401011).
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Xu, W., Yu, W., Jing, S. et al. Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Nat Hazards 65, 1379–1392 (2013). https://doi.org/10.1007/s11069-012-0414-z
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DOI: https://doi.org/10.1007/s11069-012-0414-z