Study and Prediction of Landslide in Uttarkashi, Uttarakhand, India Using GIS and ANN
American Journal of Neural Networks and Applications
Volume 3, Issue 6, December 2017, Pages: 63-74
Received: Nov. 22, 2017; Accepted: Dec. 11, 2017; Published: Jan. 22, 2018
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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
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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.
India, Uttarkashi, Landslide Susceptibility, Artificial Neural Network (ANN), GIS
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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
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