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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Biswajeet Pradhan, Waleed M. Abdulwahid

Erschienen in: Laser Scanning Applications in Landslide Assessment

Verlag: Springer International Publishing

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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.

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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.CrossRef

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.‏

Alimohammadlou, Y., Najafi, A., & Yalcin, A. (2013). Landslide process and impacts: A proposed classification method. Catena, 104, 219–232.CrossRef

Althuwaynee, O. F., & Pradhan, B. (2016). Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City.‏

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.

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.CrossRef

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.CrossRef

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.‏

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.CrossRef

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).‏

Budimir, M., Atkinson, P., & Lewis, H. (2015). A systematic review of landslide probability mapping using logistic regression. Landslides, 1–18.

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.‏

Calvo, B., & Savi, F. (2009). A real-world application of monte carlo procedure for debris flow risk assessment. Computers & Geosciences, 35(5), 967–977.CrossRef

Chung, C. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472.CrossRef

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.

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.‏

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.CrossRef

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.‏

Dilley, M. (2005). Natural disaster hotspots: A global risk analysis. World Bank Publications.

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.

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.CrossRef

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.CrossRef

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.‏

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.CrossRef

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.CrossRef

Fell, R., Ho, K. K., Lacasse, S., & Leroi, E. (2005). A framework for landslide risk assessment and management. Landslide Risk Management, 3–25.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Haneberg, W. C. (2004). A rational probabilistic method for spatially distributed landslide hazard assessment. Environmental and Engineering Geoscience, 10(1), 27–43.CrossRef

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.‏

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.‏

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.

Lee, E., & Jones, D. (2004). Landslide risk assessment.

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.CrossRef

Li, Z., Nadim, F., Huang, H., Uzielli, M., & Lacasse, S. (2010). Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides, 7(2), 125–134.CrossRef

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.CrossRef

Muthukumar, M. (2013). GIS based Geosystem response modeling for landslide vulnerability mapping parts of Nilgiris, South India. Disaster Advances, 6(7), 58–66.

Nandi, A., & Shakoor, A. (2010). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110(1), 11–20.CrossRef

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.

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.‏

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.

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.‏

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.‏

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.‏

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.

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.CrossRef

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.CrossRef

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.‏

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Varnes, D. J. (1984). Landslide hazard zonation: A review of principles and practice (No. 3).‏

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.‏

Wang, L., Sawada, K., & Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81–92.CrossRef

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.‏

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

Yao, X., & Dai, F. (2006). Support vector machine modeling of landslide susceptibility using a GIS: A case study. IAEG, 2006, 793.

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.CrossRef

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.‏

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.

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.‏

Titel: Landslide Risk Assessment Using Multi-hazard Scenario Produced by Logistic Regression and LiDAR-Based DEM
verfasst von: Biswajeet Pradhan
Waleed M. Abdulwahid
Verlag: Springer International Publishing
Buch: Laser Scanning Applications in Landslide Assessment
Print ISBN: 978-3-319-55341-2

Electronic ISBN: 978-3-319-55342-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55342-9_13