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Landslide Susceptibility Mapping: An Integrated Approach using Geographic Information Value, Remote Sensing, and Weight of Evidence Method

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Abstract

Landslide events cause significant financial losses, human casualties, and irreversible changes in the natural landscape. In this paper, we have addressed the mapping of landslides zones using the extracted Cartosat-1 Digital Elevation Model (DEM) and a knowledge-based numerical rating system. The main objective of the study is to use the Weight of Evidence (WoE) technique to produce a Landslide Hazards Zonation (LHZ) map and a Landslide Susceptible Map (LSM) to establish a relationship between landslide causality factors and past landslide locations. For the prediction and generation of the LSM, accurate DEM is extracted using the noise-free stereo images of the Cartosat-1 sensor. This research proposed a prediction model for landslide susceptibility mapping based on the combination of a knowledge-based numerical rating system, remote sensing, and the WoE technique. The WoE technique and information value method are used to calculate the weightage and ranking of each landslide causality factor. The sum of the landslide causality factor products calculates the Landslide Susceptible Index (LSI) value for every pixel. The area under concern was classified into five susceptibility classes based on the derived LSI, ranging from very low to scars. The overall prediction and forecast accuracy of the LSM generated using the WoE technique is 92.68%.

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References

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kral’ovany–Liptovsk{\`y} Mikuláš railway case study. Phys Chem Earth, Parts a/b/c 35:162–171

    Article  Google Scholar 

  • Binaghi E, Luzi L, Madella P et al (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Bopche L, Rege PP (2021) Feature-based model for landslide prediction using remote sensing and digital elevation data. In: Merchant SN, Warhade K, Adhikari D (eds) Advances in signal and data processing. Springer, Singapore, pp 299–312

    Chapter  Google Scholar 

  • Bopche L, Rege PP (2021c) Use of noise reduction filters on stereo images for improving the accuracy and quality of the digital elevation model. J Appl Remote Sens 15:1–17. https://doi.org/10.1117/1.JRS.15.014508

    Article  Google Scholar 

  • Bopche L, Rege PP, Joshi RD (2022) Landslide susceptibility mapping: an integrated approach using knowledge-based numerical rating scheme, remote sensing, and multiple overlay analysis. J Appl Remote Sens 16:1–23. https://doi.org/10.1117/1.JRS.16.014503

    Article  Google Scholar 

  • Bopche L, Rege PP (2020) Feature-Based Landslide Susceptibility and Hazard Zonation Maps using Fuzzy Overlay Analysis. In: 2020 IEEE Pune Section International Conference (PuneCon). pp 218–223

  • Bopche L, Rege PP (2021b) Feature-based model for landslide susceptibility mapping using a multi-parametric decision-making technique and the analytic hierarchy process. S{\=a}dhan{\=a} 46:1–16

  • Dahal RK, Hasegawa S, Nonomura A et al (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Masuda T, Yamanaka M (2006) Roadside slope failures in Nepal during torrential rainfall and their mitigation. Disaster Mitig debris flows, slope Fail landslides 503–514

  • Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin. Iran Nat Hazards 65:2105–2128

    Article  Google Scholar 

  • Glade T (2003) Landslide occurrence as a response to land use change: a review of evidence from New Zealand. CATENA 51:297–314

    Article  Google Scholar 

  • Gökceoglu C, Aksoy HI (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161

    Article  Google Scholar 

  • Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27

    Article  Google Scholar 

  • Keefer DK, Larsen MC (2007) Assessing landslide hazards. Science 80:1136–1138

    Article  Google Scholar 

  • Kulatilake PHSW, Park J, Balasingam P, Mckenna SA (2007) Hierarchical probabilistic regionalization of volcanism for Sengan region, Japan. Geotech Geol Eng 25:79–102

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Linkov I, Welle P, Loney D et al (2011) Use of multicriteria decision analysis to support weight of evidence evaluation. Risk Anal 31:1211–1225. https://doi.org/10.1111/j.1539-6924.2011.01585.x

    Article  Google Scholar 

  • Maheshwari BK et al (2019) Earthquake-induced landslide hazard assessment of chamoli district, uttarakhand using relative frequency ratio method. Indian Geotech J 49:108–123

    Article  Google Scholar 

  • Olia ASR, Oliaei M, Heidarzadeh H (2021) Performance of ground anchored walls subjected to dynamic and pseudo-static loading. Civ Eng J 7:974

    Article  Google Scholar 

  • Ouml M (2010) Investigation of the effect of land slope on the accuracy of digital elevation model (DEM) generated from various sources. Sci Res Essays 5:1384–1391

    Google Scholar 

  • Prasad AD, Jain K, Gairola A (2013) Article: mapping of lineaments and knowledge base preparation using geomatics techniques for part of the Godavari and Tapi Basins, India: a case study. Int J Comput Appl 70:39–47

    Google Scholar 

  • Ramadhan R et al (2021) Vertical characteristics of raindrops size distribution over Sumatra Region from global precipitation measurement observation. Emerg Sci J 5:257–268

    Article  Google Scholar 

  • Ramani SE, Pitchaimani K, Gnanamanickam VR (2011) GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis. J Mt Sci 8:505–517

    Article  Google Scholar 

  • Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 23:357–369

    Article  Google Scholar 

  • Sangle AS, Yannawar PL (2015) Morphometric analysis of watershed of sub-drainage of Godavari River in Marathwada, Ambad Region by using remote sensing. Int J Comput Appl 125:

  • Shoaib M, Yang W, Liang Y, Rehman G (2021) Stability and deformation analysis of landslide under coupling effect of rainfall and reservoir drawdown. Civ Eng J 7:1098

    Article  Google Scholar 

  • Simoni S, Zanotti F, Bertoldi G, Rigon R (2008) Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hydrol Process an Int J 22:532–545

    Article  Google Scholar 

  • Singh TN, Singh R, Singh B et al (2016) Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, India. Nat Hazards 81:2019–2030. https://doi.org/10.1007/s11069-016-2241-0

    Article  Google Scholar 

  • Sunbul F, Haner B, Mungan H et al (2021) Stability analysis of a landslide: a view with implications of microstructural soil characters. Indian Geotech J 51:647

    Article  Google Scholar 

  • Tang R-X, Kulatilake PHSW, Yan E-C, Cai J-S (2020) Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. Bull Eng Geol Environ 79:2235–2254

    Article  Google Scholar 

  • Torkashvand AM, Irani A, Sorur J (2014) The preparation of landslide map by landslide numerical risk factor (LNRF) model and geographic information system (GIS). Egypt J Remote Sens Sp Sci 17:159–170

    Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • Van Westen CJ, Van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65:167–184

    Article  Google Scholar 

  • Weed DL (2005) Weight of evidence: a review of concept and methods. Risk Anal 25:1545–1557. https://doi.org/10.1111/j.1539-6924.2005.00699.x

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the National Remote Sensing Centre (NRSC) Hyderabad, India, for providing the stereo images of the Cartosat-1 sensor. The authors are also thankful to the editor in chief and the anonymous reviewers for their thorough review, valuable comments, and constructive suggestions. This research did not receive any specific grants from funding agencies in the public, commercial, or nonprofit sectors.

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  1. Electronics and Telecommunication Engineering, College of Engineering, Pune, India

    Litesh Bopche & Priti P. Rege

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  1. Litesh Bopche
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  2. Priti P. Rege
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Correspondence to Litesh Bopche.

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Bopche, L., Rege, P.P. Landslide Susceptibility Mapping: An Integrated Approach using Geographic Information Value, Remote Sensing, and Weight of Evidence Method. Geotech Geol Eng 40, 2935–2947 (2022). https://doi.org/10.1007/s10706-022-02070-4

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