Volume 3, Issue 6, December 2017, Page: 63-74
Study and Prediction of Landslide in Uttarkashi, Uttarakhand, India Using GIS and ANN
Dilip Kumar, Department of Civil Engineering, Govind Ballabh Pant Engineering College, Pauri, India
Neha lakhwan, Department of Civil Engineering, Govind Ballabh Pant Engineering College, Pauri, India
Anita Rawat, Department of Civil Engineering, Govind Ballabh Pant Engineering College, Pauri, India
Received: Nov. 22, 2017;       Accepted: Dec. 11, 2017;       Published: Jan. 22, 2018
DOI: 10.11648/j.ajnna.20170306.12      View  1622      Downloads  79
Abstract
Landslide is defined as a slow to rapid downward movement of instable rock and debris masses under the action of gravity. Landslides are one of the major natural hazards that account for hundreds of lives besides enormous damage to properties and blocking the communication links every year. The area chosen in the present study is Uttarkashi district of Uttarakhand, suffering from frequent landslides every year. Present study focused on the possible factors that are responsible for the landslide in hilly regions of Uttarakashi, Uttarakhand. In present study we used the already existing topographical maps, satellite imageries and field work. Integrated them together using GIS and soft computing to create a database that will generate the output for the future use for prediction of susceptibility of landslide. The main aim of present study is to integrate the result of our study with spatial data, soil parameters, land inventory and used the output as a user friendly application using GIS which could predict the future susceptibility of region to landslide and% contribution of each factor for the same. In this study, layers are evaluated with the help of stability studies used to produce landslide susceptibility map by Artificial Neural Network (ANN). ArcGIS 9.3, ERDAS and Excel software have been used for zonation, and statistical analysis respectively. Database of this information layer is used to train, test, and validate the ANN model. A three-layered ANN with an input layer, two hidden layers, and one output layer is found to be optimal. Finally, an overlay analysis will be carried out by evaluating the layers obtained according to their accepted coefficient in final model.. Efficiency of the application will be calculated by the help of previously acquired data of the study area at different places and then the reliability of the application will be judged.
Keywords
India, Uttarkashi, Landslide Susceptibility, Artificial Neural Network (ANN), GIS
To cite this article
Dilip Kumar, Neha lakhwan, Anita Rawat, Study and Prediction of Landslide in Uttarkashi, Uttarakhand, India Using GIS and ANN, American Journal of Neural Networks and Applications. Vol. 3, No. 6, 2017, pp. 63-74. doi: 10.11648/j.ajnna.20170306.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Chaudhary, p. A. (2001). Landslide Hazard Assessment: Summary review and new perspective. Remote sensing Application, 58.
[2]
D. P. Kanungo, M. K. (2009). Landslide Susceptibility Zonation (LSZ). Journal of South Asia Disaster Studies, 26.
[3]
Fazal, S. (2008). GIS Basics. New Age International Publisher.
[4]
Gupta, A. (2009). Hydrogeology of Uttarkashi district. Dehradun: Central Ground Water Board.
[5]
Lee, B. P. (2009, january). Landslide risk analysis using artificial neural network. International Journal of Physical Sciences, 4 (1), 15.
[6]
]Maquaire, J. M. (2009). RISK ASSESSMENT METHODS OF LANDSLIDES. RAMSOIL, 20.
[7]
Mohammad Onagh, V. K. (2012). LANDSLIDE SUSCEPTIBILITY MAPPING IN A PART OF UTTARKASHI. International Journal of Geology, Earth and Environmental Sciences, 1-19.
[8]
Sárközy, F. (2000, october 25). Function Field Data Model Implemented by Artificial Neural Networks (ANN). Retrieved april 14, 2013, from mathworks: http://www.mathworks.com.
[9]
Vinod kumar K, T. R. (2009). landslides. In T. R. Vinod kumar K, Remote sensing Application (p. 10). New Delhi: NRSC- ISRO.
[10]
Westen, C. v. (2005). Landslide hazard and risk zonation—why is it so difficult? Springer-Verlag 2005, 40.
[11]
Havenith HB, Strom A, Cacerez F, Pirard E (2006a) Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and geotechnical approach. Landslides 3: 39-50.
[12]
Havenith HB, Torgoev I, Meleshko A, Alioshin Y, Torgoev A, Danneels G (2006b) Landslides in the Mailuu-Suu valley, Kyrgyzstan: Hazards and Impacts. Landslides 3: 137-147.
[13]
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surface Processes and Landforms 29: 687–711.
[14]
Soille P (1999) Morphological Image Analysis. Principles and Applications. Springer-Verlag, Berlin. 316 p.
[15]
Dilip Kumar, Sushil Kr. Himanshu, Geographical Information Based Evaluation System for Drought, American Journal of Biological and Environmental Statistics. Vol. 3, No. 4, 2017, pp. 49-53. doi: 10.11648/j.ajbes.20170304.12
Browse journals by subject