Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model

A. Kumar *, R. Kalita**, A. Sharma ***, V. Ranga****, J. S. Rawat *****
*-***** Centre of Excellence for NRDMS in Uttarakhand, Department of Geography, Kumaun University, SSJ Campus, Almora,
Periodicity:December - February'2018
DOI : https://doi.org/10.26634/jpr.4.4.14129

Abstract

Landslides pose a great risk to life and property, therefore landslide susceptibility assessment is of vital importance, especially, in the hilly terrain. The key objective of this study is to generate a landslide susceptibility map through integrating weightages of different categories of the landslide causative factors derived from Statistical Information Value Model (SIVM) under Geographic Information Systems (GIS) environment. Several causative factors, such as slope, slope aspect, geology, drainage proximity, structural feature proximity, Landu Use/ Land Cover (LU/LC), NDVI, curvature, topographic wetness index, stream power index, road proximity, and relative relief were identified in this study area resulting in slope failures to a great extent. The existing landslides were mapped using remotely sensed data and field survey which were then divided into 70% (model training, i.e. calibration) and 30% (model testing, i.e. validation) data sets. Finally, the Landslide susceptibility map derived from statistical information value model has been divided into five equal classes, namely Very Low, Low, Moderate, High, and Very High. The accuracy of the model was evaluated using Receiver Operation Characteristic (ROC) curve, which resulted in 0.86 areas under curve. The area under curve figure reflects that the prediction accuracy of model is 86% and the results obtained will be useful for the policymakers in the study area for the generation of key plans and decision-making tasks.

Keywords

Remote Sensing, Geographic Information System (GIS), Receiver Operation Characteristic (ROC) Curve, Statistical Information Value Model (SIVM).

How to Cite this Article?

Kumar, A., Kalita, R., Sharma, A., Ranga, V., and Rawat, J. S. (2018). Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model. i-manager’s Journal on Pattern Recognition, 4(4), 10-20. https://doi.org/10.26634/jpr.4.4.14129

References

[1]. Adhikari, M. (2011). Bivariate Statistical Analysis of Landslide Susceptibility in Western Nepal (Master Thesis in Geosciences, University of Oslo).
[2]. Ahmed, B. (2014). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077- 1095.
[3]. Carrara, A., Cardinali, M., Detti , R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427-445.
[4]. Chalkias, C., Ferentinou, M., & Polykretis, C. (2014). GIS supported landslide susceptibility modeling at regional scale: An expert-based fuzzy weighting method. ISPRS International Journal of Geo-Information, 3(2), 523- 539.
[5]. Chung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451-472.
[6]. Das, I., Stein, A., Kerle, N., & Dadhwal, V. K. (2011). Probabilistic Landslide Hazard Assessment using Homogeneous Susceptible Units (HSU) along a National Highway Corridor in the Northern Himalayas, India. Landslides, 8(3), 293-308.
[7]. 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.
[8]. Ghosh, S. (2011). Knowledge guided empirical prediction of landslide hazard (Doctral Dissertation, University of Twente).
[9]. Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., Galli, M., & Ardizzone, F. (1999). Landslide hazard evaluation: An aid to a sustainable development. Geomorphology, 31(1-4), 181-216.
[10]. Jaafari, A., Najafi, A., Pourghasemi, H. R., Rezaeian, J., & Sattarian, A. (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4), 909-926.
[11]. Long, N. T. (2008). Landslide susceptibility mapping of the mountainous area in A Luoi district, Thua Thien Hue province, Vietnam (Doctoral Dissertation, Vrije Universiteit Brussel, Belgium).
[12]. Mancini, F., Ceppi, C., & Ritrovato, G. (2010). GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Natural Hazards and Earth System Sciences, 10(9), 1851-1864.
[13]. Pourghasemi, H. R., Mohammady, M., & Pradhan, B. (2012). Landslide susceptibility mapping using index entropy and conditional probability model in GIS: Safarood Basin, Iran. Science Direct, 97, 71-84.
[14]. 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.
[15]. 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.
[16]. Sarkar, S., Kanungo, D. P., Patra, A. K., & Kumar, P. (2008). GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science, 5(1), 52-62.
[17]. Yin, K. A. (1988). Statistical prediction model for slope instability of metamorphosed rocks. Proceedings of the 5th International Symposium on Landslides.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.