Weitere Kapitel dieses Buchs durch Wischen aufrufen
The assessment of landslide susceptibility is closely associated with the spatial distribution of landslides. In the present study, both frequency ratio (FR) model and modified information value (MIV) model were applied to analyse landslide susceptibility in Darjeeling Himalaya. Both the models dealt with the relationship between landslide phenomena and landslide conditioning factors. To perform the models data layers, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were taken into account. Each and every class/category of landslide conditioning factor contributes a relative importance in landslide occurrences. To prepare all the data layers, Landsat TM image, SRTM DEM, Google earth image, and some authorized maps were processed in accordance with ArcMap 10.1 and Erdas imagine 9.2. To obtain the relative significance of each class/category of landslide conditioning factors, frequency ratio (FR) value and modified information value (MIV) were estimated and accordingly the ranking values were assigned to each class/category to integrate all the data layers on GIS platform as well as to prepare landslide susceptibility map of Darjeeling Himalaya. The derived landslides susceptibility maps by using frequency ratio model and modified information value model were verified being considering the area under curve (AUC) of ROC curve and frequency ratio plot. The AUC value of ROC curve of FR model and MIV model was 0.746 and 0.769, respectively. The AUC value represents the prediction accuracy of landslide susceptibility map was 74.6% for frequency ratio model and 76.9% for modified information value model.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Akbar T, Ha S (2011) Landslide hazard zoning along Himalaya KaghanValley of Pakistan-by integration of GPS, GIS, and remote sensingtechnology. Landslides, 8(4), 527–540.
Avinash, K. G., & Ashamanjari, K. G. (2010). A GIS and frequency ratio based landslide susceptibility mapping: Aghnashini river catchment, Uttara Kannada, India. International Journal of Geomatics and Geosciences, 1(3), 343–354.
Atkinson, P. M., & Massari, R. (1998). Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24, 373–385. CrossRef
Bagherzadeh, A., & Mansouri Daneshvar, M. R. (2012). Mapping of landslide hazard zonationusing GIS at Golestan watershed, northeast of Iran. Arabian Journal of Geosciences, 6, 3377–3388. CrossRef
Balsubramani, K., & Kumaraswamy, K. (2013). Application of geospatial technology andinformation value technique in landslide hazard zonation mapping: A case study of Giri Valley, Himachal Pradesh. Disaster Advances, 6, 38–47.
Bui, D. T., Lofman, O., Revhaug, I., & Dick, O. (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59(3), 1413–1444.
Caiyan WU and Jianping Q (2009) Relationship between landslides and lithology in the Three Gorges Reservoir area based on GIS AND Information Value Model. Higher Education Press and Springer New York 4(2), 165–170
Champatiray, P. (2000). Perationalization of costeffective methodology for landslide hazard zonation using RS and GIS: IIRS initiative. In P. Roy, C. Van Westen, V. Jha, & R. Lakhera (Eds.), Natural disasters and their mitigation; remote sensing and geographical information system perspectives (pp. 95–101). Dehradun, India: Indian Institute of Remote Sensing.
Choi, J., Oh, H. J., Lee, H. J., Lee, C., & Lee, S. (2011). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using aster images and GIS. Engineering Geology, 124, 12–23. CrossRef
Donati, L., & Turrini, M. C. (2002). An objective and method to rank the importance of the factors predisposing to landslides with the GIS methodology, application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63(3-4), 277–289. CrossRef
Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75, 229–250. CrossRef
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. Journal of Geomorphology, 31, 181–216. London: Elsevier. CrossRef
Intarawichian, N., & Dasananda, S. (2011). Frequency Ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environment and Earth Science, 64, 2271–2285. CrossRef
Ilanloo, M. (2011). A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Procedia - Social and Behavioral Sciences, 19, 668–676. CrossRef
Jibson, W. R., Edwin, L. H., & John, A. M. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58, 271–289. CrossRef
Kanungo, D., Arrora, M., Sarkar, S., & Gupta, R. (2009). Landslide Susceptibility Zonation (LSZ) mapping -A review. Journal of South Asia Disaster Studies, 2, 81–105.
Karim, S., Jalileddin, S., & Ali, M. T. (2011). Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle-East Journal of Scientific Research, 9(5), 578–583.
Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44, 820–833. CrossRef
Lee, S., & Pradhan, B. (2006a). Landslide hazard assessment at Cameron Highland Malaysia using frequency ratio and logistic regression models. Geophysical Research Abstracts, 8, SRef ID: 1607-7962/gra/EGU06-A-03241.
Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the DamreiRomel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50, 847–855. CrossRef
Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990. CrossRef
Lee, S., & Pradhan, B. (2006b). Probabilistic landslide risk mapping at Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661–672. CrossRef
Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequencyratio and logistic regression models. Landslides, 4(1), 33–41. CrossRef
Mandal, S., & Maiti, R. (2011). Landslide susceptibility analysis of shivkhola watershed, darjeeling: A remote sensing & GIS based analytical hierarchy process (AHP). Journal of Indian Society of Remote Sensing. https://doi.org/10.10007/s12524-011-0160-9.
Mandal, S., & Maiti, R. (2013). Integrating the Analytical Hierarchy Process (AHP) and the Frequency Ratio (FR) model in landslide susceptibility mapping of Shiv-khola Watershed, Darjeeling Himalaya. International Journal of Disaster Risk Science, 4(4), 200–212. https://doi.org/10.1007/s13753-013-0021-y. CrossRef
Muthu, K., & Petrou, M. (2007). Landslide hazard mapping using an ExpertSystem and a GIS. Transactions on Geoscience and Remote Sensing, 45(2), 522–531. CrossRef
Nithya, E. S., & Prasanna, R. P. (2010). An integrated approach with GIS and remote sensing technique for landslide zonation. International Journal of Geomatics and Geosciences, 1(1), 66–75.
Oliveira, S. C., Zêzere, J. L., Catalão, J., & Nico, G. (2015). The contribution of PSInSAR interferometry to landslide hazard in weak rocks dominated areas. Landslides, 12, 703–719. CrossRef
Pandey, A., Dabral, P. P., Chowdhary, V. M., & Yadav, N. K. (2008). Landslide hazard zonation using remote sensing and GIS: A case study of Dikrong river basin, Arunachal Pradesh, India. Environmental Geology, 54, 1517–1529. CrossRef
Pistocchi, A., Luzi, L., & Napolitano, P. (2002). The use of predictive modelling techniques for optimal exploitation of spatial databases: A case study in landslide hazard mapping with expert system-like methods. Environmental Geology, 41, 765–775. CrossRef
Poudyal, C. P., Chang, C., Oh, H. J., & Lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environmental Earth Sciences, 61, 1049–1064. CrossRef
Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropapagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25, 747–759. CrossRef
Porghasemi, H. (2007). Landslide hazard zoning statistical frequency ratio method in the basin Safarood. M.Sc Thesis, TarbiatModarres University, Noor, p. 1386.
Pradhan, B. (2010). Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45, 1244–1256. CrossRef
Pradhan, B., & Lee, S. (2009). Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environmental Earth Sciences, 60, 1037–1054. CrossRef
Rowbotham, D., & Dudycha, D. N. (1998). GIS Modelling of slope stability in Phewa Tal Watershed, Nepal. Geomorphology, 26, 151–170. CrossRef
Sarkar, S., & Kanungo, D. P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5), 617–625. CrossRef
Sarkar, S., Kanungo, D., Patra, A., & Kumar, P. (2006). Disaster mitigation of debris flows, slope failures and landslides: GIS based landslide susceptibility mapping case study in Indian Himalaya (pp. 617–624). Tokyo, Japan: Universal Academy Press.
Sharma, L., Patel, N., Ghosh, M., & Debnath, P. (2009). Geographical information system based landslide probabilistic model with trivariate approach - A case study in Sikkim Himalaya. Eighteenth United Nations Regional Cartographic Conference for Asia and the Pacific, UN, Bankok, Economic and Social Council.
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
Vijith, H., Rejith, P. G., & Madhu, G. (2009). Using Info Val Method and GIS techniques for the spatial modelling of landslides susceptibility in the Upper catchment of River Meenachil in Kerala. Indian Society of Remote Sensing, 37, 241–250. CrossRef
Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslide and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197–207. CrossRef
- Frequency Ratio (FR) Model and Modified Information Value (MIV) Model in Landslide Susceptibility Assessment and Prediction
- Chapter 3
Systemische Notwendigkeit zur Weiterentwicklung von Hybridnetzen