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Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh, India

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Abstract

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.

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References

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides 9(1):93106

    Article  Google Scholar 

  • Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):11271143

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):2144

    Article  Google Scholar 

  • Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32(4):269–277

    Article  Google Scholar 

  • Anbazhagan S, Ramesh V (2014) Landslide hazard zonation mapping in ghat road section of Kolli hills, India. J Mountain Sci 11(5):13081325

    Article  Google Scholar 

  • 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(1):1531

    Google Scholar 

  • Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):7381

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4):432445

    Google Scholar 

  • Bai S, Lu G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62(1):139149

    Article  Google Scholar 

  • Ballard TM, Willington RP (1975) Slope instability in relation to timber harvesting in the Chilliwack provincial forest. For Chron 51(2):5963

    Article  Google Scholar 

  • Barredo J, Benavides A, Hervas J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J App Earth Obser Geoinf 2(1):923

    Google Scholar 

  • Bhushan N, Rai K (2004) Strategic decision making and the analytic hierarchy process (IX, p. 172)

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45(1):5572

    Article  Google Scholar 

  • Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411423

    Article  Google Scholar 

  • Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

    Article  Google Scholar 

  • Ciurleo M, Cascini L, Calvello M (2017) A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils. Eng Geol 223:7181

    Article  Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48(4):349364

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63(2):397406

    Article  Google Scholar 

  • Dahal RK, Bhandary NP, Hasegawa S, Yatabe R (2014) Topo-stress based probabilistic model for shallow landslide susceptibility zonation in the Nepal Himalaya. Environ Earth Sci 71(9):38793892

    Article  Google Scholar 

  • Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):6587

    Article  Google Scholar 

  • Demir G, Aytekin M, Akgun A, Ikizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the north Anatolian fault zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65(3):14811506

    Article  Google Scholar 

  • Deoja B, Dhital MR, Thapa B, Wagner A (1991) Mountain risk engineering handbook, vol 2. International Centre for Integrated Mountain Development (ICIMOD), Patan (ISBN: 92-9115-354-0)

    Google Scholar 

  • Dou J, Chang KT, Chen S, Yunus AP, Liu JK, Xia H, Zhu Z (2015) Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sens 7(4):43184342

    Article  Google Scholar 

  • Duarte RM, Marquıinez J (2002) The influence of environmental and lithologic factors on rockfall at a regional scale: an evaluation using GIS. Geomorphology 43(1):117136

    Google Scholar 

  • Ellen SD, Mark RK, Cannon SH, Knifong DL (1993) Map of debris-flow hazard in the Honolulu District of Oahu, Hawaii. US Geological Survey 93─213. https://doi.org/10.3133/ofr93213

  • Elmahdy SI, Mohamed MM (2014) Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area, United Arab Emirates. Geocarto Intern 29(4):433450

    Article  Google Scholar 

  • ESRI F (2012) What is the jenks optimization method. http://support.esri.com/en/knowledgebase/techarticles/detail/26442

  • Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208221

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1):6272

    Google Scholar 

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:111

    Article  Google Scholar 

  • Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81(1):6583

    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(1):1127

    Google Scholar 

  • Gong P (1996) Geological mapping. Photogramm Eng Rem Sens 62(5):513523

    Google Scholar 

  • Gorsevski PV, Brown MK, Panter K, Onasch CM, Simic A, Snyder J (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13(3):467484

    Article  Google Scholar 

  • Greenbaum D, Tutton M, Bowker MR, Browne TJ, Buleka J, Greally KB, O’Connor EA (1995) Rapid methods of landslide hazard mapping: papua new guinea case study. Brit Geol Surv 27:1–112

    Google Scholar 

  • Greenway DR (1987) Vegetation and slope stability. In: Anderson MG, Richards KS (eds) Slope stability: geotechnical engineering and geomorphology. Wiley, Chichester, pp 187–230

    Google Scholar 

  • Gryta JJ, Bartholomew MJ (1983) Debris–avalanche types features in Watauga County, North Carolina. In: Lewis SE (ed) Carolina Geological Society guidebook 1–22, Article (5). North Carolina Division of Land Resources, Boone, 21–23 Oct 1983

  • Guerra AJT, Fullen MA, Jorge MDCO, Bezerra JFR, Shokr MS (2017) Slope processes, mass movement and soil erosion: A review. Pedosphere 27(1):2741

    Article  Google Scholar 

  • Guillard C, Zezere J (2012) Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: the case of Loures Municipality. Environ Manag 50(4):721735

    Article  Google Scholar 

  • Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himalayas Eng Geol 28(1):119131

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1):181216

    Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1):166184

    Google Scholar 

  • Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198208

    Article  Google Scholar 

  • Jaafari A, Rezaeian J, Omrani MSO (2017) Spatial prediction of slope failures in support of forestry operations safety. Croatian J Forest Eng: J for Theory Application Forestry Eng 38(1):107118

    Google Scholar 

  • Jaiswal RK, Saxena R, Mukherjee S (1999) Application of remote sensing technology for land use/land cover change analysis. J Indian Soc of Remote Sens 27(2):123128

    Article  Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425439

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398408

    Article  Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1):1728

    Google Scholar 

  • Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271

    Article  Google Scholar 

  • Kumar A, Krishna AP (2018) Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto Int 33(2):105129

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):14771491

    Article  Google Scholar 

  • Lee S, Dan NT (2005) Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol 48(6):778787

    Article  Google Scholar 

  • Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes Landforms 28(12):13611376

    Article  Google Scholar 

  • Lee S, Choi J, Woo I (2004) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea. Geosci J 8(1):51

    Article  Google Scholar 

  • Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin. Korea Landslides 4(4):327338

    Google Scholar 

  • Lin ML, Tung CC (2004) A GIS-based potential analysis of the landslides induced by the Chi-Chi earthquake. Eng Geol 71(1):6377

    Google Scholar 

  • Liu P, Li Z, Hoey T, Kincal C, Zhang J, Zeng Q, Muller JP (2013) Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China. Int J App Earth Obs Geoinfo 21:253264

    Google Scholar 

  • Lohnes RA, Handy RL (1968) Slope angles in friable loess. The J Geol 76(3):247258

    Article  Google Scholar 

  • Mani S, Saranaathan SE (2017) Landslide hazard zonation mapping on meso-scale in SH-37 ghat section, Nadugani, Gudalur, the Nilgiris, India. Arabian J Geosciences 10(7):161

    Article  Google Scholar 

  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3):379400

    Google Scholar 

  • Moebs NN, Sames GP (1987) The character of five selected LANDSAT lineaments in Southwestern Pennsylvania Report of Investigations. U.S. Department of the Interior 9104. https://www.osti.gov/biblio/5365799

  • Mohammadi A, Tabatabaeefar A, Shahin S, Rafiee S, Keyhani A (2008) Energy use and economical analysis of potato production in Iran a case study: Ardabil province. Energy Conver Management 49(12):3566–3570

    Article  Google Scholar 

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1):1120

    Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3):171191

    Google Scholar 

  • Ocakoglu F, Gokceoglu C, Ercanoglu M (2002) Dynamics of a complex mass movement triggered by heavy rainfall: a case study from NW Turkey. Geomorphology 42(3):329341

    Google Scholar 

  • Oh HJ, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environ Geol 57(3):641

    Article  Google Scholar 

  • Oh HJ, Park NW, Lee SS, Lee S (2012) Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping. Int J Remote Sens 33(10):32113231

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149:5263

    Article  Google Scholar 

  • Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75(3):185

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Aghda SF (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749779

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350365

    Article  Google Scholar 

  • Qari M (1991) Application of Landsat TM data to geological studies, Al-Khabt area, southern Arabian Shield. Photogrammetric Eng Remote Sens 57(4):421429

    Google Scholar 

  • Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76(14):499

    Article  Google Scholar 

  • Roodposhti MS, Rahimi S, Beglou MJ (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat hazards 73(1):7795

    Article  Google Scholar 

  • Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63(1):4963

    Article  Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234281

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process: planning. Priority Setting. Resource Allocation MacGraw-Hill International Book Company New York 287

    Google Scholar 

  • Saaty TL (1990a) How to make a decision: the analytic hierarchy process. European J Operational Research 48(1):926

    Google Scholar 

  • Saaty TL (1990b) Decision making for leaders: the analytic hierarchy process for decisions in a complex world RWS publications, Pittsburgh (ISBN 0-9620317-8-X)

    Google Scholar 

  • Saaty TL (1994) How to make a decision: the analytic hierarchy process. Interfaces 24(6):1943

    Article  Google Scholar 

  • Saaty TL (2006) Rank from comparisons and from ratings in the analytic hierarchy/network processes. European J Operational Research 168(2):557570

    Article  Google Scholar 

  • Saaty TL, Vargas LG (2012) Models, methods, concepts and applications of the analytic hierarchy process, vol 175. Springer, Berlin. https://doi.org/10.1007/978-1-4614-3597-6

    Book  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(2):357369

    Article  Google Scholar 

  • Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Eng Remote Sens 70(5):617–625

    Article  Google Scholar 

  • Satty A, Thomas L (2000) Fundamentals of decision making and priority theory Pittsburgh

  • Scaioni M (2013) Remote sensing for landslide investigations: from research into practice. Remote Sens 5(11):5488–5492. https://doi.org/10.3390/rs511548

    Article  Google Scholar 

  • Selby M (1993) Hillslope materials and processes. Oxford Univ. Press, Oxford

    Google Scholar 

  • Sharma RK, Mehta BS (2012) Macro-zonation of landslide susceptibility in Garamaura-Swarghat-Gambhar section of national highway 21, Bilaspur District, Himachal Pradesh (India). Nat Hazards 60(2):671688

    Google Scholar 

  • Sharma RK, Mehta BS, Jamwal CS (2013) Cut slope stability evaluation of NH-21 along Nalayan-Gambhrola section, Bilaspur district, Himachal Pradesh, India. Nat Hazards 66(2):249270

    Article  Google Scholar 

  • Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and land use, vol 18. American Geophysical Union, Washington. https://doi.org/10.1029/WM011

    Book  Google Scholar 

  • Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S (2012) Prediction of landslides using ASTER imagery and data mining models. Advanc Space Res 49(5):978–993

    Article  Google Scholar 

  • Strahler AN (1964) Part II Quantitative geomorphology of drainage basins and channel networks. Handbook of applied hydrology. McGraw-Hill, New York, pp 4–39

    Google Scholar 

  • Thanh LN, De Smedt F (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district, Thua Thien Hue Province, Vietnam. Environ Earth Sci 66(7):17391752

    Article  Google Scholar 

  • Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92(1):3859

    Google Scholar 

  • Tian Y, XiaO C, Liu Y, Wu L (2008) Effects of raster resolution on landslide susceptibility mapping: a case study of Shenzhen. Sci China Ser E: Technol Sci 51(2):188198

    Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Natural hazards 30(3):399419

    Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geology 102(3):112131

    Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. Spec Rep 176:1133

    Google Scholar 

  • Waters P (1990) Methodology of lineament analysis for hydrogeological investigations. Chapter 11:197214

    Google Scholar 

  • Xu C, Xu X, Dai F, Wu Z, He H, Shi F, Wu X, Xu S (2013) Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Nat Hazards 68(2):883900

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274287

    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(4):821836

    Article  Google Scholar 

  • Zhang G, Cai Y, Zheng Z, Zhen J, Liu Y, Huang K (2016) Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena 142: 233244

    Article  Google Scholar 

  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561577

    Google Scholar 

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Kumar, A., Sharma, R.K. & Bansal, V.K. Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh, India. Environ Earth Sci 77, 719 (2018). https://doi.org/10.1007/s12665-018-7896-2

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