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Statistical Approaches for Landslide Susceptibility Assessment and Prediction pp 77–105Cite as

Frequency Ratio (FR) Model and Modified Information Value (MIV) Model in Landslide Susceptibility Assessment and Prediction

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Abstract

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.

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References

  • 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.

    Google Scholar 

  • Arora, M., Das Gupta, A., & Gupta, R. (2004). An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalaya. International Journal of Remote Sensing, 25, 559–572. https://doi.org/10.1080/0143116031000156819.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Atkinson, P. M., & Massari, R. (1998). Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24, 373–385.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Google Scholar 

  • 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.

    Google Scholar 

  • Champatiray, P., Dimri, S., Lakhera, R., & Sati, S. (2007). Fuzzy based methods for landslide hazard assessment in active seismic zone of Himalaya. Landslides, 4, 101–110. https://doi.org/10.1007/s1034600600686.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.

    Article  Google Scholar 

  • Lee, S., & Pradhan, B. (2006b). Probabilistic landslide risk mapping at Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661–672.

    Article  Google Scholar 

  • Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequencyratio and logistic regression models. Landslides, 4(1), 33–41.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mandal, B., & Mandal, S. (2016). Assessment of mountain slope instability in the lish river basin of eastern Darjeeling Himalaya using frequency ratio model (FRM) Model. Earth Systems and Environment, 2(121), 1–14. https://doi.org/10.1007/s40808-016-0169-8.

    Article  Google Scholar 

  • Mandal, B., & Mandal, S. (2017). Landslide susceptibility mapping using modified information value model in the lish river basin of Darjiling Himalaya. Spatial Information Research, 2(7), 1–14. https://doi.org/10.1007/s41324-017-0096-4.

    Article  Google Scholar 

  • Muthu, K., & Petrou, M. (2007). Landslide hazard mapping using an ExpertSystem and a GIS. Transactions on Geoscience and Remote Sensing, 45(2), 522–531.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pereira, S., Zezere, J., & Bateira, C. (2012). Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models. Natural Hazards and Earth System Sciences, 12, 979–988. https://doi.org/10.5194/nhess129792012.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Porghasemi, H. (2007). Landslide hazard zoning statistical frequency ratio method in the basin Safarood. M.Sc Thesis, TarbiatModarres University, Noor, p. 1386.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rowbotham, D., & Dudycha, D. N. (1998). GIS Modelling of slope stability in Phewa Tal Watershed, Nepal. Geomorphology, 26, 151–170.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, H., & Sassa, K. (2005). Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology, 47, 956–966. https://doi.org/10.1007/s0025400512252.

    Article  Google Scholar 

  • Wang, Q., et al. (2015). Landslide susceptibility mapping based on selected optimal combination of landslide predisposing factors in a large catchment. Sustainability, 7, 16653–16669. https://doi.org/10.3390/su71215839.

    Article  Google Scholar 

  • Zezere, J. (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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India

    Sujit Mandal

  2. University of Gour Banga, Mokdumpur, West Bengal, India

    Subrata Mondal

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  1. Sujit Mandal
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  2. Subrata Mondal
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Mandal, S., Mondal, S. (2019). Frequency Ratio (FR) Model and Modified Information Value (MIV) Model in Landslide Susceptibility Assessment and Prediction. In: Statistical Approaches for Landslide Susceptibility Assessment and Prediction. Springer, Cham. https://doi.org/10.1007/978-3-319-93897-4_3

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