Abstract
The present research paper is an attempt to assess the vulnerability of Rampur Tehsil to landslides using weighted overlay and fuzzy logic methods. Causative factors such as land use, land cover, slope, geology, soil, and geomorphology have been used to assess landslide vulnerability. Survey of India Toposheets, Geological Survey of India Maps, ASTER GDEM, and LANDSAT 8 OLI/TIRS sensors are used as data sources. The causative factors were analyzed and processed in GIS environment. Fuzzy logic and weighted overlay method have been used to categorize the vulnerability zones of the study area. The weightages were assigned based on fuzzy logic rule of for macroscale landslide mapping and weighted overlay scale ranging from 1 to 5 for very low vulnerability to very high vulnerability. From the results, it can be interpreted that most of the study areas come under very high vulnerability class. The fuzzy values for each class vary from 0.6 to 0.8 for high vulnerability and from 0.81 to 0.96 for very high vulnerability class. About 57% of the area comes under very high vulnerability class, and rest 47% accounts for high vulnerability class. When it comes to weighted overlay model, nearly 80.24% and 13.68% of the area fall and under high and moderately vulnerable category. The rest minor quantities fall under very high and low categories.
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References
Martha TR, Kerle N, van Westen CJ, Jetten V, Vinod Kumar K (2012) Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogramm Remote Sens 67(1):105–119. https://doi.org/10.1016/j.isprsjprs.2011.11.004
Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach-A case study from the Ramganga catchment, Himalayas. Eng Geol 28(1–2):119–131. https://doi.org/10.1016/0013-7952(90)90037-2
Panikkar SV, Subramanyan V (1996) A geomorphic evaluation of the landslides around Dehradun and Mussoorie, Uttar Pradesh, India. Geomorphology 15(2):169–181. https://doi.org/10.1016/0169-555X(95)00121-K
Champati Ray PK, Dimri S, Lakhera RC, Sati Sa (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4(2):101–111. https://doi.org/10.1007/s10346-006-0068-6
Prakash S (2016) Historical records of socio-economically significant Landslides in India. J South Asian Stud 4(2):177–204
Anbalagan R, Parida S (2013) Geoenvironmental problems due to harmony landslide in Garhwal Himalaya, Uttarakhand, India. Int J Emerg Technol Adv Eng 3(3):553–559
Anbalagan R, Singh B (1996) Landslide hazard and risk assessment mapping of mountainous terrains-a case study from Kumaun Himalaya, India. Eng Geol 43:237–246. https://doi.org/10.1016/S0013-7952(96)00033-6
Kanungo D, Arrora M, Sarkar S, Gupta R (2009) Landslide Susceptibility Zonation (LSZ) mapping-a review. J South Asia Disaster Stud 2:81–105
Naithani A (2007) Macro landslide hazard zonation mapping using univariate statistical analysis in parts of Garhwal Himalaya. J Geol Soc India 70:353–368
Rawat MS, Uniyal DP, Dobhal R, Joshi V, Rawat BS, Bartwal A, Aswal A (2015) Study of landslide hazard zonation in Mandakini Valley, Rudraprayag district, Uttarakhand using remote sensing and GIS. Curr Sci 109(1):158–170
Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 23(2):357–369
Anbalagan R, Kumar R, Lakshmanan K, Parida S, Neethu S (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung valley, Sikkim. Geoenvironmental Disasters 2(1):6. https://doi.org/10.1186/s40677-014-0009-y
Bibi T, Gul Y, Abdul Rahman A, Riaz M (2016) Landslide susceptibility assessment through fuzzy logic inference system (FLIS). Int Arch Photogramm, Remote Sens Spat Inf sciences—ISPRS Arch 42(4W1):355–360. https://doi.org/10.5194/isprs-archives-XLII-4-W1-355-2016
Leonardi G, Palamara R, Cirianni F (2016) Landslide susceptibility mapping using a fuzzy approach. Procedia Eng 161:380–387. https://doi.org/10.1016/j.proeng.2016.08.578
Mijani N, Samani NN (2017) Comparison of fuzzy-based models in landslide hazard mapping XLII(October):7–10
Rahaman S, Abdul S, Aruchamy S, Jegankumar R (2014) Geospatial approach on landslide hazard zonation mapping using multicriteria decision analysis: a study on Coonoor and Ooty, Part of Kallar Watershed, the Nilgiris, Tamil Nadu. Int Arch Photogramm, Remote Sens Spat Inf Sci-ISPRS Arch 40(8):1417–1422. https://doi.org/10.5194/isprsarchives-XL-8-1417-2014
Othman, AN, Mohd W. Naim WM, Noraini S (2012) GIS based multi-criteria decision making for landslide hazard zonation. Procedia—Soc Behav Sci 35(December 2011):595–602. https://doi.org/10.1016/j.sbspro.2012.02.126
Raghuvanshi TK, Negassa L, Kala PM (2015) GIS based grid overlay method versus modeling approach—a comparative study for landslide hazard zonation (LHZ) in meta robi District of West Showa Zone in Ethiopia. Egypt J Remote Sens Space Sci 18(2):235–250. https://doi.org/10.1016/j.ejrs.2015.08.001
Elmahdy SI, Marghany MM, Mohamed MM (2016) Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia. Geomatics, Nat Hazards Risk 7(1):345–359. https://doi.org/10.1080/19475705.2014.904825
Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong metropolitan area, Bangladesh. Landslides 12(6):1077–1095. https://doi.org/10.1007/s10346-014-0521-x
Ding MT, Miao C (2015) GIS-based assessment of vulnerability to landslide hazards in Lushan earthquake-stricken areas. J Risk Anal Cris Response 5(2):93–106. https://doi.org/10.2991/jrarc.2015.5.2.3
Michael EA, Samanta S (2016) Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques. Model Earth Syst Environ 2(2):81–88. https://doi.org/10.1007/s40808-016-0141-7
Wang, J, Peng XG (2009) GIS-based landslide hazard zonation model and its application. Procedia Earth Planet Sci 1(1):1198–1204. https://doi.org/10.1016/j.proeps.2009.09.184
Shit PK, Bhunia GS, Maiti R (2016) Potential landslide susceptibility mapping using weighted overlay model (WOM). Model Earth Syst Environ 2(1):11–21. https://doi.org/10.1007/s40808-016-0078-x
Lari S, Frattini P, Crosta GB (2014) A Probabilistic approach for landslide hazard analysis. Eng Geol 182(PA):3–14. https://doi.org/10.1016/j.enggeo.2014.07.015
Panikkar S, Subramaniyan V (1997) Landslide hazard analysis of the area around Dehra Dun and Mussoorie, Uttar Pradesh. Curr Sci 73:1117–1123
Bureau of Indian Standards (1998) Preparation of landslide hazard zonation maps in mountainous terrain—Guidelines (Part2-Macrozonation), vol 14496, 2nd edn. BIS, New Delhi, pp 1–19
Acknowledgements
The research work done is a part of NRDMS-DST funded research project. We would like to express our sincerest gratitude to NRDMS-DST, GOI, New Delhi, India, for funding this research project.
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Prakasam, C., Aravinth, R., Kanwar, V.S., Nagarajan, B. (2020). Comparative Study Between Weighted Overlay and Fuzzy Logic Models for Landslide Vulnerability Mapping—A Case Study of Rampur Tehsil, Himachal Pradesh. In: Kanwar, V., Shukla, S. (eds) Sustainable Civil Engineering Practices. Lecture Notes in Civil Engineering, vol 72. Springer, Singapore. https://doi.org/10.1007/978-981-15-3677-9_16
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