Abstract
Landslides are occurring more frequently in China under the conditions of extreme rainfall and changing climate, according to News reports. Landslide hazard assessment remains an international focus on disaster prevention and mitigation, and it is an important step for compiling and quantitatively characterizing landslide damages. This paper collected and analyzed the historical landslide events data of the past 60 years in China. Validated by the frequencies and distributions of landslides, nine key factors (lithology, convexity, slope gradient, slope aspect, elevation, soil property, vegetation coverage, flow, and fracture) are selected to construct landslide susceptibility (LS) empirical models by back-propagation artificial neural network method. By integrating landslide empirical models with surface multi-source geospatial and remote sensing data, this paper further performs a large-scale LS assessment throughout China. The resulting landslide hazard assessment map of China clearly illustrates the hot spots of the high landslide potential areas, mostly concentrated in the southwest. The study implements a complete framework of multi-source data collecting, processing, modeling, and synthesizing that fulfills the assessment of LS and provides a theoretical basis and practical guide for predicting and mitigating landslide disasters potentially throughout China.
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References
Bălteanu D, Chendeş V, Sima M, Enciu P (2010) A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology 124(3):102–112
Braspenning PJ, Thuijsman F, Weijters AJMM (1995) Artificial neural networks: an introduction to ANN theory and practice. Springer, Berlin
Chacón J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. B Eng Geol Environ 65(4):341–411
Chauhan S, Sharma M, Arora M, Gupta N (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. Int J Appl Earth Obs 12(5):340–350
Che VB, Kervyn M, Suh C, Fontijn K, Ernst G, del Marmol MA, Trefois P, Jacobs P (2012) Landslide susceptibility assessment in Limbe (sw Cameroon): a field calibrated seed cell and information value method. Catena 92:83–98
Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using aster images and GIS. Eng Geol 124:12–23
Dai F, Lee C, Ngai Y (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87
Dunning SA, Mitchell WA, Rosser NJ, Petley DN (2007) The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir earthquake of 8 October 2005. Eng Geol 93(3–4):130–144
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (west black sea region, turkey). Eng Geol 75(3):229–250
Fernandes AM, Utkin AB, Lavrov AV, Vilar RM (2004) Development of neural network committee machines for automatic forest fire detection using lidar. Pattern Recogn 37(10):2039–2047
Ghosh S (2011) Knowledge guided empirical prediction of landslide hazard. ITC, Faculty of Geo-Information Science and Earth Observation, Enschede
Gong P (1996) Geological mapping. Photogramm Eng Rem Sens 62(5):513–523
Günther A, Reichenbach P, Malet J-P, Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2012) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, central Italy. Geomorphology 31(1):181–216
He KQ, Li XR, Yan XQ, Dong G (2008) The landslides in the three gorges reservoir region, china and the effects of water storage and rain on their stability. Environ Geol 55(1):55–63
Henderson LJ (2004) Emergency and disaster: pervasive risk and public bureaucracy in developing nations. Public Org Rev 4(2):103–119
Hong Y, Adler R, Huffman G (2007a) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43(2):245–256
Hong Y, Adler RF, Huffman G (2007b) An experimental global prediction system for rainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEE T Geosci Remote 45(6):1671–1680
Iwahashi J, Kamiya I, Yamagishi H (2012) High-resolution dems in the study of rainfall-and earthquake-induced landslides: use of a variable window size method in digital terrain analysis. Geomorphology 153–154:29–38
Kumar K, Sati D, Goyal N, Mathur S (2005) Landslide hazard potential analysis using GIS, Patalganga Valley, Garhwal, Western Himalayan region of India. Eur J Sci Res 45(3):346–366
Li C, Ma T, Zhu X, Li W (2011) The power–law relationship between landslide occurrence and rainfall level. Geomorphology 130(3):221–229
Manunta P, Brugioni M, Casagli N, Colombo D, Deflorio AM, Farina P, Ferretti A, Goutier E, Graf K, Haeberle J, Lateltin O, Meloni E, Mayoraz R, Montini G, Moretti S, Paganini M, Palazzo F, Spina D, Sulli L, Strozzi T (2011) Slam, a service for landslide monitoring based on eo-data. http://earth.esa.int/workshops/fringe03/participants/485/paper_paper_manunta_SLAM.pdf
Mathew J, Jha V, Rawat G (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6(1):17–26
Mondini A, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115(7):1743–1757
Montgomery DR, Dietrich WE, Heffner JT (2002) Piezometric response in shallow bedrock at cb1: implications for runoff generation and landsliding. Water Resour Res 38(12):1274
Moreiras SM (2005) Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina. Geomorphology 66(1):345–357
Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3(2):159–173
Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3):171–191
Park NW (2008) Geostatistical integration of different sources of elevation and its effect on landslide hazard mapping. Korean J Remote Sens 24(5):453–462
Parry S (2011) Geomorphological mapping: methods and applications. Elsevier, Amsterdam
Peruccacci S, Brunetti MT, Luciani S, Vennari C, Guzzetti F (2011) Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy. Geomorphology 139–140:79–90
Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349
Pradhan B, Singh R, Buchroithner M (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37(4):698–709
Qin S, Jiao JJ, Wang S (2002) A nonlinear dynamical model of landslide evolution. Geomorphology 43(1):77–85
Refice A, Capolongo D (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci-UK 28(6):735–749
Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and land use. American Geophysical Union, Washington, DC
Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Eng Geol 108(3):237–251
Wang Z, Fukao Y, Pei S (2009) Structural control of rupturing of the Mw7.9 Wenchuan Earthquake, China. Earth Planet Sci Left 279(1–2):131–138
Wang WD, Guo J, Fang LG, Chang XS (2012) A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS. Environ Earth Sci 65(6):1705–1714
Wibowo A, Kafle B, Kermani AM, Lam NTK, Wilson JL, Gad EF Damage in the 2008 china earthquake. In: Proceedings of Australian earthquake engineering society conference
Wu W, Sidle RC (1995) A distributed slope stability model for steep forested watersheds. Water Resour Res 31(8):2097–2110
Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72(1):1–12
Zhang GP, Xu J, Bi BG (2009) Relations of landslide and debris flow hazards to environmental factors. Chin J Appl Ecol 20(3):653–658
Acknowledgments
The work described in this paper is funded by National Basic Research Program of China (Project No. 2013CB733204), National Natural Science Foundation of China (No.41201380) and Key Laboratory of Advanced Engineering Surveying of NASMG (TJES1010), and is also supported by the Center of Spatial Information Science and Sustainable Development Applications, Tongji University.
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A major outcome of this work is landslide historical data collection over 60 years and the development of LS in China, through multi-factor empirical method.
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Liu, C., Li, W., Wu, H. et al. Susceptibility evaluation and mapping of China’s landslides based on multi-source data. Nat Hazards 69, 1477–1495 (2013). https://doi.org/10.1007/s11069-013-0759-y
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DOI: https://doi.org/10.1007/s11069-013-0759-y