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Landslide Risk Assessment Using Multi-hazard Scenario Produced by Logistic Regression and LiDAR-Based DEM

  • Biswajeet PradhanEmail author
  • Waleed M. Abdulwahid
Chapter

Abstract

The rapid urban development and population growths worldwide push threats to the people because of landslides and other mass movements. Landslide is one of the natural disasters causing significant damages to lives and properties.

Keywords

Landslide Susceptibility Support Vector Machine Model Landslide Hazard Landslide Occurrence Landslide Susceptibility Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, 9(1), 93–106.CrossRefGoogle Scholar
  2. Akgun, A., Kıncal, C., & Pradhan, B. (2012). Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environmental Monitoring and Assessment, 184(9), 5453–5470.‏Google Scholar
  3. Alimohammadlou, Y., Najafi, A., & Yalcin, A. (2013). Landslide process and impacts: A proposed classification method. Catena, 104, 219–232.CrossRefGoogle Scholar
  4. Althuwaynee, O. F., & Pradhan, B. (2016). Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City.‏Google Scholar
  5. Althuwaynee, O. F., Pradhan, B., & Ahmad, N. (2014a). Estimation of rainfall threshold and its use in landslide hazard mapping of kuala lumpur metropolitan and surrounding areas. Landslides, 1–15.Google Scholar
  6. Althuwaynee, O. F., Pradhan, B., Park, H.-J., & Lee, J. H. (2014b). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.CrossRefGoogle Scholar
  7. Bai, S., Wang, J., Lü, G., Zhou, P., Hou, S., & Xu, S. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three gorges area, China. Geomorphology, 115(1), 23–31.CrossRefGoogle Scholar
  8. Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical Geosciences, 44(1), 47–70.‏Google Scholar
  9. Bell, R., & Glade, T. (2004). Quantitative risk analysis for landslides? Examples from Bíldudalur, NW-Iceland. Natural Hazards and Earth System Science, 4(1), 117–131.CrossRefGoogle Scholar
  10. Bornaetxea, T., Antigüedad, I., & Ormaetxea, O. (2016, April). Shallow landslide susceptibility model for the Oria river basin, Gipuzkoa province (North of Spain). Application of the logistic regression and comparison with previous studies. In EGU General Assembly Conference Abstracts (Vol. 18, p. 7715).‏Google Scholar
  11. Budimir, M., Atkinson, P., & Lewis, H. (2015). A systematic review of landslide probability mapping using logistic regression. Landslides, 1–18.Google Scholar
  12. Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361–378.‏Google Scholar
  13. Calvo, B., & Savi, F. (2009). A real-world application of monte carlo procedure for debris flow risk assessment. Computers & Geosciences, 35(5), 967–977.CrossRefGoogle Scholar
  14. Chung, C. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472.CrossRefGoogle Scholar
  15. Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., et al. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263.Google Scholar
  16. Costanzo, D., Chacón, J., Conoscenti, C., Irigaray, C., & Rotigliano, E. (2014). Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides, 11(4), 639–653.‏Google Scholar
  17. Demir, G., Aytekin, M., & Akgun, A. (2015). Landslide susceptibility mapping by frequency ratio and logistic regression methods: An example from Niksar-Resadiye (Tokat, Turkey). Arabian Journal of Geosciences, 8(3), 1801–1812.CrossRefGoogle Scholar
  18. Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., et al. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165.‏Google Scholar
  19. Dilley, M. (2005). Natural disaster hotspots: A global risk analysis. World Bank Publications.Google Scholar
  20. Dou, J., Oguchi, T., Hayakawa, Y. S., Uchiyama, S., Saito, H., & Paudel, U. (2014). GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu area, Central Japan. In Landslide science for a safer geoenvironment (pp. 419–424). Berlin: Springer.Google Scholar
  21. Dragićević, S., Lai, T., & Balram, S. (2015). GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International, 45, 114–125.CrossRefGoogle Scholar
  22. Erener, A., & Düzgün, H. S. (2013). A regional scale quantitative risk assessment for landslides: Case of Kumluca watershed in Bartin, Turkey. Landslides, 10(1), 55–73.CrossRefGoogle Scholar
  23. Erener, A., Mutlu, A., & Düzgün, H. S. (2016). A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Engineering Geology, 203, 45–55.‏Google Scholar
  24. Falaschi, F., Giacomelli, F., Federici, P., Puccinelli, A., Avanzi, G., Pochini, A., et al. (2009). Logistic regression versus artificial neural networks: Landslide susceptibility evaluation in a sample area of the Serchio river valley, Italy. Natural Hazards, 50(3), 551–569.CrossRefGoogle Scholar
  25. Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3), 99–111.CrossRefGoogle Scholar
  26. Fell, R., Ho, K. K., Lacasse, S., & Leroi, E. (2005). A framework for landslide risk assessment and management. Landslide Risk Management, 3–25.Google Scholar
  27. Ghosh, S., van Westen, C. J., Carranza, E. J. M., Jetten, V. G., Cardinali, M., Rossi, M., et al. (2012). Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Engineering Geology, 128, 49–62.CrossRefGoogle Scholar
  28. 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(6–8), 1059–1079.CrossRefGoogle Scholar
  29. Guzzetti, F., Ardizzone, F., Cardinali, M., Galli, M., Reichenbach, P., & Rossi, M. (2008). Distribution of landslides in the Upper Tiber River basin, Central Italy. Geomorphology, 96(1), 105–122.CrossRefGoogle Scholar
  30. Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Science, 6(1), 115–131.CrossRefGoogle Scholar
  31. Haneberg, W. C. (2004). A rational probabilistic method for spatially distributed landslide hazard assessment. Environmental and Engineering Geoscience, 10(1), 27–43.CrossRefGoogle Scholar
  32. Hong, H., Pradhan, B., Bui, D. T., Xu, C., Youssef, A. M., & Chen, W. (2016). Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: A case study at Suichuan area (China). Geomatics, Natural Hazards and Risk, 1–26.‏Google Scholar
  33. Huang, J., Lyamin, A., Griffiths, D., Krabbenhoft, K., & Sloan, S. (2013). Quantitative risk assessment of landslide by limit analysis and random fields. Computers and Geotechnics, 53, 60–67.CrossRefGoogle Scholar
  34. Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2014). Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment, 152, 150–165.CrossRefGoogle Scholar
  35. Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2015). Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, Malaysia. Geomatics, Natural Hazards and Risk, 6(8), 741–759.CrossRefGoogle Scholar
  36. Kanungo, D., Arora, M., Gupta, R., & Sarkar, S. (2008). Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides, 5(4), 407–416.CrossRefGoogle Scholar
  37. Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425–439.‏Google Scholar
  38. Kritikos, T., & Davies, T. (2014). Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: Application to western southern Alps of New Zealand. Landslides, 1-25.Google Scholar
  39. Lee, E., & Jones, D. (2004). Landslide risk assessment.Google Scholar
  40. Lee, M. L., Ng, K. Y., Huang, Y. F., & Li, W. C. (2014). Rainfall-induced landslides in Hulu Kelang area, Malaysia. Natural hazards, 70(1), 353–375.CrossRefGoogle Scholar
  41. Li, Z., Nadim, F., Huang, H., Uzielli, M., & Lacasse, S. (2010). Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides, 7(2), 125–134.CrossRefGoogle Scholar
  42. Moreiras, S. M., & Sepúlveda, S. A. (2015). Megalandslides in the Andes of central Chile and Argentina (32°–34° S) and potential hazards. Geological Society, London, Special Publications, 399(1), 329–344.CrossRefGoogle Scholar
  43. Muthukumar, M. (2013). GIS based Geosystem response modeling for landslide vulnerability mapping parts of Nilgiris, South India. Disaster Advances, 6(7), 58–66.Google Scholar
  44. Nandi, A., & Shakoor, A. (2010). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110(1), 11–20.CrossRefGoogle Scholar
  45. Opolot, E. (2013). Application of remote sensing and geographical information systems in flood management: A review. Research Journal of Applied Sciences, Engineering and Technology, 5(10), 1884–1894.Google Scholar
  46. Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197.‏Google Scholar
  47. Pedrazzini, A., Humair, F., Jaboyedoff, M., & Tonini, M. (2015). Characterisation and spatial distribution of gravitational slope deformation in the upper rhone catchment (western Swiss Alps). Landslides, 1–19.Google Scholar
  48. Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349–369.‏Google Scholar
  49. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.‏Google Scholar
  50. Pradhan, B., Abokharima, M. H., Jebur, M. N., & Tehrany, M. S. (2014). Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards, 73(2), 1019–1042.‏Google Scholar
  51. Pradhan, B., & Lee, S. (2009). Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Sciences, 3(11), 1–15.Google Scholar
  52. Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054.CrossRefGoogle Scholar
  53. Pradhan, B., & Youssef, A. M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geosciences, 3(3), 319–326.CrossRefGoogle Scholar
  54. Regmi, N. R., Giardino, J. R., McDonald, E. V., & Vitek, J. D. (2014). A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides, 11(2), 247–262.‏Google Scholar
  55. Regmi, N. R., Giardino, J. R., & Vitek, J. D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1), 172–187.CrossRefGoogle Scholar
  56. Solaimani, K., Mousavi, S. Z., & Kavian, A. (2013). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6(7), 2557–2569.CrossRefGoogle Scholar
  57. Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.CrossRefGoogle Scholar
  58. Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369.CrossRefGoogle Scholar
  59. Tournadour, E., Mulder, T., Borgomano, J., Hanquiez, V., Ducassou, E., & Gillet, H. (2015). Origin and architecture of a mass transport complex on the northwest slope of little Bahama Bank (Bahamas): Relations between off-bank transport, bottom current sedimentation and submarine landslides. Sedimentary Geology, 317, 9–26.CrossRefGoogle Scholar
  60. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.CrossRefGoogle Scholar
  61. van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3), 112–131.CrossRefGoogle Scholar
  62. Varnes, D. J. (1984). Landslide hazard zonation: A review of principles and practice (No. 3).‏Google Scholar
  63. Vranken, L., Vantilt, G., Van Den Eeckhaut, M., Vandekerckhove, L., & Poesen, J. (2015). Landslide risk assessment in a densely populated hilly area. Landslides, 12(4), 787–798.‏Google Scholar
  64. Wang, L., Sawada, K., & Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81–92.CrossRefGoogle Scholar
  65. Wu, X., Ren, F., & Niu, R. (2014). Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environmental Earth Sciences, 71(11), 4725–4738.‏Google Scholar
  66. Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70–80.CrossRefGoogle Scholar
  67. Xu, J., Nyerges, T. L., & Nie, G. (2014). Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology. International Journal of Geographical Information Science, 28(1), 185–205.CrossRefGoogle Scholar
  68. Xu, C., Xu, X., Dai, F., Wu, Z., He, H., Shi, F., et al. (2013). Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Natural Hazards, 68(2), 883–900.CrossRefGoogle Scholar
  69. Yang, W., Peng, Z., Wang, B., Li, Z., & Yuan, S. (2015). Velocity contrast along the rupture zone of the 2010 Mw6. 9 Yushu, China, earthquake from fault zone head waves. Earth and Planetary Science Letters, 416, 91–97.CrossRefGoogle Scholar
  70. Yang, M., Wu, M., & Liu, J. (2014). Estimating landslide-induced riverbed roughness variation by using lidar data. Journal of Marine Science and Technology, 22(4), 424–429.Google Scholar
  71. Yao, X., & Dai, F. (2006). Support vector machine modeling of landslide susceptibility using a GIS: A case study. IAEG, 2006, 793.Google Scholar
  72. Yao, X., Tham, L., & Dai, F. (2008). Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572–582.CrossRefGoogle Scholar
  73. Youssef, A. M., Al-Kathery, M., & Pradhan, B. (2015). Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosciences Journal, 19(1), 113–134.‏Google Scholar
  74. Yusof, N. M., Pradhan, B., Shafri, H. Z. M., Jebur, M. N., & Yusoff, Z. (2015). Spatial landslide hazard assessment along the jelapang corridor of the north-south expressway in Malaysia using high-resolution airborne LiDAR data. Arabian Journal of Geosciences, 1–12.Google Scholar
  75. Zhang, M., Cao, X., Peng, L., & Niu, R. (2016). Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China. Environmental Earth Sciences, 75(11), 1–11.‏Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity Putra MalaysiaSerdangMalaysia

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