Abstract
The hilly regions are developing at a very rapid rate. The anthropogenic activities due to road construction increase the instability of slopes along highways. Aim of this study is to prepare a landslide susceptibility map along State Highway 32. Landslide susceptibility maps along road section prove a good tool for effective mitigation and management of the landslide hazards. The parameters considered in this study are slope, aspect, elevation, drainage density, lithology, soil and distance from fault. Analytic hierarchy process (AHP) is used for evaluating various parameters and ranking them. Landslide Susceptibility Index (LSI) is calculated by using weighted linear combination (WLC) technique. The final landslide susceptibility map is divided into four categories from low to very high susceptibility zones. It is found that around 65% of the area lies under high and very high landslide susceptibility. The results of the study can be used by the urban planners, transportation planners and highway engineers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytic hierarchy process (AHP) in Tehri reservoir rim region, Uttrakhand. J Geol Soc India 87:1–16
Pandey VK, Sharma MC (2017) Probabilistic landslide susceptibility mapping along Tripti to Ghuttu highway corridor, Garhwal Himalaya (India). Remote Sens Appl Soc Environ 8
Demir G (2018) Landslide susceptibility mapping by using statistical analysis in the North Anatolian Fault Zone (NAFZ) on the northern part of Suşehri Town, Turkey. Nat Hazards 92:133–154
Othman AN, Naim WM, Noraini WM (2012) GIS-based multi-criteria decision making for landslide hazard zonation. Procedia-Soc Behav Sci 35:595–602
Ahmed B (2014) Landslide susceptibility mapping using multi-criteria evaluation technique in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095
Ding Q, Chen W, Hong H (2016) Application of frequency ratio, weight of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32:619–639
Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349
Lee S (2010) Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea. Int J Remote Sens 28:4363–4383
Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81:245–263
Highland M.L (2008) The landslide handbook: a guide to understanding landslides. Geological Survey, U.S., pp 112–120
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98
Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43(1):27–29
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Panchal, S., Shrivastava, A.K. (2020). Landslide Susceptibility Mapping Along Highway Corridors in GIS Environment. In: Ahmed, S., Abbas, S., Zia, H. (eds) Smart Cities—Opportunities and Challenges. Lecture Notes in Civil Engineering, vol 58. Springer, Singapore. https://doi.org/10.1007/978-981-15-2545-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-2545-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2544-5
Online ISBN: 978-981-15-2545-2
eBook Packages: EngineeringEngineering (R0)