Skip navigation
Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18805
Title: Comparison of support vector machine and artificial neural network for delineating debris covered glacier
Authors: Nijhawan R.
Das, Josodhir D.
Balasubramanian R.
Nayak M.
Singh D.
Mishra D.K.
Unal A.
Joshi A.
Published in: Proceedings of Communications in Computer and Information Science
Abstract: Glacier mapping accuracy plays very important role in studies like mass balance of glacier, water resource management and in understanding the health of the glacier. Several of the present glaciers are covered with debris of different thickness. So it becomes difficult to distinguish debris covered glacier from the adjacent valley rock, alone with the use of optical data because of the same reflectance in visible to near infrared region. In this paper we have trained Support vector machine (SVM) and Artificial neural network (ANN) on several parameters such as slope, surface curvature, thermal data and also on several texture parameter, such as variance, skewness, entropy, homogeneity, mean and dissimilarity. Then both the algorithms were applied on the part of the alaknanda basin. It was observed that both ANN and SVM produced good results, with accuracy of SVM slightly higher than that of ANN algorithm. © Springer Nature Singapore Pte Ltd. 2016.
Citation: Proceedings of Communications in Computer and Information Science, (2016), 550- 557
URI: https://doi.org/10.1007/978-981-10-3433-6_66
http://repository.iitr.ac.in/handle/123456789/18805
Issue Date: 2016
Publisher: Springer Verlag
Keywords: Artificial neural network
Debris
Glacier
Support vector machine
Debris
Glaciers
Infrared devices
Neural networks
Water management
Water resources
ANN algorithm
Debris covered glaciers
Different thickness
Mapping accuracy
Near infrared region
Surface curvatures
Texture parameters
Waterresource
ISBN: 9.78981E+12
ISSN: 18650929
Author Scopus IDs: 57192176367
7202105464
7103127999
Author Affiliations: Nijhawan, R., Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Das, J., Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Balasubramanian, R., Depar
Corresponding Author: Nijhawan, R.; Department of Earthquake Engineering, Indian Institute of Technology RoorkeeIndia; email: rahul.deq2014@iitr.ac.in
Appears in Collections:Conference Publications [EQ]

Files in This Item:
There are no files associated with this item.
Show full item record


Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.