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Survey of spatial and temporal landslide prediction methods and techniques

  • An, Hyunuk (Department of Agricultural & Rural Engineering Chungnam National University) ;
  • Kim, Minseok (International Water Resources Research Institute, Chungnam National University) ;
  • Lee, Giha (Department of Construction & Disaster Prevention Engineering Kyungpook National University) ;
  • Viet, Tran The (Department of Construction & Disaster Prevention Engineering Kyungpook National University)
  • 투고 : 2016.12.13
  • 심사 : 2016.12.29
  • 발행 : 2016.12.31

초록

Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

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참고문헌

  1. Acharya G, Smedt FD, Long NT. 2005. Assessing landslide hazard in GIS: A case study from Rasuwa, Nepal. Bull. Bulletin of Engineering Geology and the Environment 65:99-107.
  2. Akgun A. 2011. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at Izmir, Turkey. Landslides 9: 93-106.
  3. Aleotti P. 2004. A warning system for rainfall-induced shallow failures. Engineering Geology Rainfall-triggered landslides and debris flows 73:247-265. https://doi.org/10.1016/j.enggeo.2004.01.007
  4. Alvioli M, Baum RL. 2016. Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface Environmental Modelling & Software 81:122-135. https://doi.org/10.1016/j.envsoft.2016.04.002
  5. An H, Viet TT, Lee G, Kim Y, Kim M, Noh S, Noh J. 2016. Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow. Environmental Modelling & Software 85:172-183. https://doi.org/10.1016/j.envsoft.2016.08.009
  6. Arora MK, Gupta ASD, Gupta RP. 2004. An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. International Journal of Remote Sensing 25:559-572. https://doi.org/10.1080/0143116031000156819
  7. Ayalew L, Yamagishi H, Marui H, Kanno T. 2005. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology 81:432-445. https://doi.org/10.1016/j.enggeo.2005.08.004
  8. Ayalew L, Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan. Geomorphology 65:15-31. https://doi.org/10.1016/j.geomorph.2004.06.010
  9. Balteanu D, Chendes V, Sima M, Enciu P. 2010. A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology, Recent advances in landslide investigation 124:102-112. https://doi.org/10.1016/j.geomorph.2010.03.005
  10. Baum RL, Godt JW, Savage WZ. 2010. Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration. Journal of Geophysical Research: Earth Surface 115:F03013.
  11. Baum RL, Godt JW. 2009. Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7:259-272.
  12. Baum RL, Savage WZ, Godt JW. 2008. TRIGRS-A fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0. Geological Survey Open-File Report.
  13. Berti M, Martina MLV, Franceschini S, Pignone, S, Simoni A, Pizziolo M. 2012. Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach. Journal of Geophysical Research: Earth Surface 117:F04006.
  14. Blahut J, van Westen CJ, Sterlacchini S. 2010. Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119:36-51. https://doi.org/10.1016/j.geomorph.2010.02.017
  15. Caine N. 1980. The rainfall intensity: Duration control of shallow landslides and debris flows. Geografiska Annaler. Series A, Physical Geography 62:23-27. https://doi.org/10.2307/520449
  16. Campbell RH. 1975. Soil slips, debris flows, and rainstorms in the Santa Monica mountains and vicinity, Southern California. U.S. Govt. Print. Off.
  17. Cannon SH, Gartner JE, Wilson RC, Bowers JC, Laber JL. 2008. Storm rainfall conditions for floods and debris flows from recently burned areas in southwestern Colorado and Southern California. Geomorphology 96:20.
  18. Cardinali M, Reichenbach P, Guzzetti F, Ardizzone F, Antonini G, Galli M, Cacciano M, Castellani M, Salvati P. 2002. A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy. Natural Hazards and Earth System Science 2:57-72. https://doi.org/10.5194/nhess-2-57-2002
  19. Carrara A, Cardinali M, Guzzetti F, Reichenbach P. 1995. GIS technology in mapping landslide hazard. In Geographical information systems in assessing natural hazards, Advances in natural and technological hazards research edited by Carrara A, Guzzetti F. pp. 135-175. Springer, Netherlands.
  20. Catani F, Casagli N, Ermini L, Righini G, Menduni G. 2005. Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2:329-342. https://doi.org/10.1007/s10346-005-0021-0
  21. Champatiray PK, Dimri S, Lakhera RC, Sati S. 2006. Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101.
  22. Chang KT, Chiang SH, Hsu ML. 2007. Modeling typhoon-and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology 89:335-347. https://doi.org/10.1016/j.geomorph.2006.12.011
  23. Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP. 2004. Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers & Geosciences 30:429-443. https://doi.org/10.1016/j.cageo.2003.08.013
  24. Clerici A, Perego S, Tellini C, Vescovi P. 2002. A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349-364. https://doi.org/10.1016/S0169-555X(02)00079-X
  25. Conoscenti C, Di Maggio C, Rotigliano E. 2008. GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology, GIS technology and models for assessing landslide hazard and risk 94:325-339. https://doi.org/10.1016/j.geomorph.2006.10.039
  26. Das I, Stein A, Kerle N, Dadhwal VK. 2011. Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India. Landslides 8:293-308. https://doi.org/10.1007/s10346-011-0257-9
  27. Ercanoglu M, Gokceoglu C, Asch TWJV. 2004. Landslide Susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards 32:1-23. https://doi.org/10.1023/B:NHAZ.0000026786.85589.4a
  28. Ercanoglu M. 2005. Landslide susceptibility assessment of SE Bartin (West black sea region, Turkey) by artificial neural networks. Natural Hazards Earth System Science 5:979-992. https://doi.org/10.5194/nhess-5-979-2005
  29. Erener A, Duzgun HSB. 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55-68. https://doi.org/10.1007/s10346-009-0188-x
  30. Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M. 2008. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172-191. https://doi.org/10.1016/j.geomorph.2007.06.001
  31. Ghosh S, Westen CJ, Carranza EJM, Ghoshal TB, Sarkar NK, Surendranath M. 2009. A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility. Journal of the Geological Society of India 74:625. https://doi.org/10.1007/s12594-009-0167-9
  32. Glade T, Crozier M, Smith P. 2000. Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical "Antecedent daily rainfall model." Pure and Applied Geophysics 157:1059-1079. https://doi.org/10.1007/s000240050017
  33. Gomes RAT, Guimaraes RF, de Carvalho J, Fernandes NF, do Amaral Junior EV. 2013. Combining spatial models for shallow landslides and debris-flows prediction. Remote Sensing 5:2219-2237. https://doi.org/10.3390/rs5052219
  34. Goswami R, Mitchell NC, Brocklehurst SH. 2011. Distribution and causes of landslides in the eastern Peloritani of NE Sicily and western Aspromonte of SW Calabria, Italy. Geomorphology 132:111-122. https://doi.org/10.1016/j.geomorph.2011.04.036
  35. Greco R, Giorgio M, Capparelli G, Versace P. 2013. Early warning of rainfall-induced landslides based on empirical mobility function predictor. Engineering Geology 153:68-79. https://doi.org/10.1016/j.enggeo.2012.11.009
  36. Guidicini G, Iwasa OY. 1997. Tentative correlation between rainfall and landslides in a humid tropical environment. Bulletin of the International Association of Engineering Geology 16:13-20.
  37. Guo X, Cui P, Li Y, 2013. Debris flow warning threshold based on antecedent rainfall: A case study in Jiangjia Ravine, Yunnan, China. Journal of Mountain Science 10:305-314. https://doi.org/10.1007/s11629-013-2521-z
  38. Guzzetti F, Peruccacci S, Rossi M, Stark CP. 2007a. The rainfall intensity-duration control of shallow landslides and debris flows: An update. Landslides 5:3-17.
  39. Guzzetti F, Peruccacci S, Rossi M, Stark CP. 2007b. Rainfall thresholds for the initiation of landslides in Central and Southern Europe. Meteorology and Atmospheric Physics 98:239-267. https://doi.org/10.1007/s00703-007-0262-7
  40. Hong Y, Hiura H, Shino K, Sassa K, Suemine A, Fukuoka H, Wang G. 2005. The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island, Japan. Landslides 2:97-105. https://doi.org/10.1007/s10346-004-0043-z
  41. Huabin W, Gangjun L, Weiya X, Gonghui W. 2005. GIS-based landslide hazard assessment: An overview. Progress in Physical Geography 29:548-567. https://doi.org/10.1191/0309133305pp462ra
  42. Huang J, Ju NP, Liao YJ, Liu DD. 2015. Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method. Natural Hazards Earth System Science 15: 2715-2723. https://doi.org/10.5194/nhess-15-2715-2015
  43. Iverson RM. 1997. The physics of debris flows. Reviews of Geophysics 35:245-296. https://doi.org/10.1029/97RG00426
  44. Iverson RM. 2000. Landslide triggering by rain infiltration. Water Resources Research 36:1897-1910. https://doi.org/10.1029/2000WR900090
  45. Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown WM, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS. 1987. Real-time landslide warning during heavy rainfall. Science 238:921-925. https://doi.org/10.1126/science.238.4829.921
  46. Kim D, Lee EJ, Ahn B, Im S. 2014. Landslide Susceptibility mapping using a grid-based infiltration transient model in mountainous regions. In Landslide science for a safer geoenvironment edited by Sassa K, Canuti P, Yin Y. pp. 425-429. Springer International Publishing.
  47. Lee CT, Huang CC, Lee JF, Pan KL, Lin ML, Dong JJ. 2008. Statistical approach to storm event-induced landslides susceptibility. Natural Hazards Earth System Science 8:941-960. https://doi.org/10.5194/nhess-8-941-2008
  48. Lee G, Oh S, An H, Jung K. 2012. A comparative analysis on slope stability using specific catchment area calculation. Journal of Korea Water Resources Association 45:643-656. https://doi.org/10.3741/JKWRA.2012.45.7.643
  49. Lee JH, Park HJ. 2014. GIS-based probabilistic analysis of shallow landslide susceptibility using a transient hydrogeological model and Monte Carlo simulation. In Landslide science for a safer geoenvironment edited by Sassa K, Canuti P, Yin Y. pp. 451-456. Springer International Publishing.
  50. Lee S. 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing 26: 1477-1491. https://doi.org/10.1080/01431160412331331012
  51. Liao Z, Hong Y, Kirschbaum D, Adler RF, Gourley JJ, Wooten R. 2010. Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)'s predictive skill for hurricane-triggered landslides: A case study in Macon County, North Carolina. Natural Hazards 58:325-339. doi:10.1007/s11069-010-9670-y
  52. Liu CN, Wu CC. 2007. Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach. Environmental Geology 55:907-915.
  53. Lu N, Likos WJ. 2006. Suction stress characteristic curve for unsaturated soil. Journal of Geotechnical and Geoenvironmental Engineering 132:131-142. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:2(131)
  54. Lumb P. 1975. Slope failures in Hong Kong. Quarterly Journal of Engineering Geology and Hydrogeology 8:31-65. https://doi.org/10.1144/GSL.QJEG.1975.008.01.02
  55. Martha TR, van Westen CJ, Kerle N, Jetten V, Vinod Kumar K. 2013. Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology 184:139-150. https://doi.org/10.1016/j.geomorph.2012.12.001
  56. Michel GP, Kobiyama M, Goerl RF. 2014. Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, Southern Brazil. Journal of Soils and Sediments 14:1266-1277. https://doi.org/10.1007/s11368-014-0886-4
  57. Montgomery DR, Dietrich WE. 1994. A physically based model for the topographic control on shallow landsliding. Water Resources Research 30:1153-1171. https://doi.org/10.1029/93WR02979
  58. Montrasio L, Valentino R, Losi GL. 2011. Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale. Natural Hazards Earth System Science 11:1927-1947. https://doi.org/10.5194/nhess-11-1927-2011
  59. Nefeslioglu AT, Duman TY, Durmaz S. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology, GIS technology and models for assessing landslide hazard and risk 94:401-418. https://doi.org/10.1016/j.geomorph.2006.10.036
  60. Neuhauser B, Damm B, Terhorst B. 2011. GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model. Landslides 9:511-528.
  61. Ohlmacher GC, Davis JC. 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology 69:331-343. https://doi.org/10.1016/S0013-7952(03)00069-3
  62. Osanai N, Shimizu T, Kuramoto K, Kojima S, Noro T. 2010. Japanese early-warning for debris flows and slope failures using rainfall indices with Radial Basis Function Network. Landslides 7:325-338. https://doi.org/10.1007/s10346-010-0229-5
  63. Pack RT, Tarboton DG, Goodwin CN. 1999. SINMAP 2.0 - A stability index approach to terrain stability hazard mapping, User's Manual.
  64. Pardeshi SD, Autade SE, Pardeshi SS. 2013. Landslide hazard assessment: Recent trends and techniques. SpringerPlus 2:523. https://doi.org/10.1186/2193-1801-2-523
  65. Parise M. 2002. Landslide hazard zonation of slopes susceptible to rock falls and topples. Natural Hazards Earth System Science 2:37-49. https://doi.org/10.5194/nhess-2-37-2002
  66. Peres DJ, Cancelliere A. 2016. Estimating return period of landslide triggering by Monte Carlo simulation. Journal of Hydrology 541:256-271. https://doi.org/10.1016/j.jhydrol.2016.03.036
  67. Piacentini D, Troiani F, Soldati M, Notarnicola C, Savelli D, Schneiderbauer S, Strada C. 2012. Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy). Geomorphology 151-152:196-206. https://doi.org/10.1016/j.geomorph.2012.02.003
  68. Pradhan AMS, Oh JR, Jung MS, Kim YT. 2014. Predictive capability of deterministic and statistical models in weathered granite soil watershed, In Landslide science for a safer geoenvironment edited by Sassa K, Canuti P, Yin Y. pp. 507-512. Springer International Publishing.
  69. Pradhan B, Lee S. 2009. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13-30.
  70. Preuth T, Glade T, Demoulin A. 2010. Stability analysis of a human-influenced landslide in eastern Belgium. Geomorphology, Landslide geomorphology in a changing environment 120:38-47. https://doi.org/10.1016/j.geomorph.2009.09.013
  71. Saadatkhah N, Kassim A, Lee LM. 2014. Hulu Kelang, Malaysia regional mapping of rainfall-induced landslides using TRIGRS model. Arabian Journal of Geosciences 8:3183-3194.
  72. Sarkar S, Kanungo DP, Mehrotra GS. 1995. Landslide hazard zonation: A case study in Garhwal Himalaya, India. Mountain Research and Development 15:301-309. https://doi.org/10.2307/3673806
  73. Schicker R, Moon V. 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161-162:40-57. https://doi.org/10.1016/j.geomorph.2012.03.036
  74. Segoni S, Battistini A, Rossi G, Rosi A, Lagomarsino D, Catani F, Moretti S. Casagli, N. 2015. Technical note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds. Natural Hazards Earth System Science 15:853-861. https://doi.org/10.5194/nhess-15-853-2015
  75. Segoni S, Rosi A, Rossi G, Catani F, Casagli N. 2014. Analysing the relationship between rainfalls and landslides to define a mosaic of triggering thresholds for regional-scale warning systems. Natural Hazards Earth System Science 14:2637-2648. https://doi.org/10.5194/nhess-14-2637-2014
  76. Simoni S, Zanotti F, Bertoldi G, Rigon R. 2008. Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hydrological Processess 22:532-545. https://doi.org/10.1002/hyp.6886
  77. Terhorst B, Kreja R. 2009. Slope stability modelling with SINMAP in a settlement area of the Swabian Alb. Landslides 6:309-319. https://doi.org/10.1007/s10346-009-0167-2
  78. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB. 2012. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology:171-172, 12-29. https://doi.org/10.1016/j.geomorph.2012.04.023
  79. Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J. 2009. Combined landslide inventory and susceptibility assessment based on different mapping units: An example from the Flemish Ardennes, Belgium. Natural Hazards Earth System Science 9:507-521. https://doi.org/10.5194/nhess-9-507-2009
  80. Wang HB, Sassa K. 2005. Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology 47:956-966. https://doi.org/10.1007/s00254-005-1225-2
  81. Westen CJ, van Rengers N, Terlien MTJ, Soeters R. 1997. Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geologische Rundschau 86:404-414. https://doi.org/10.1007/s005310050149
  82. Zezere JL, Vaz T, Pereira S, Oliveira SC, Marques R, Garcia RAC. 2014. Rainfall thresholds for landslide activity in Portugal: A state of the art. Environmental Earth Sciences 73:2917-2936.
  83. Zezere JL. 2002. Landslide susceptibility assessment considering landslide typology. A case study in the area north of Lisbon (Portugal). Natural Hazards and Earth System Sciences 2:73-82. https://doi.org/10.5194/nhess-2-73-2002

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