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Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

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Abstract

This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management.

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References

  • Abul Hasanat M, Ramachandram D and Mandava R 2010 Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: A comparative study; Artif. Intell. Rev. 34 291–308.

    Article  Google Scholar 

  • Ahmed S 2009 Slope stability analysis using GIS and numerical modeling techniques (unpublished M.Sc. thesis), Vrije Universiteit, Brussel.

  • Akgun A and Bulut F 2007 GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region; Environ. Geol. 51(8) 1377–1387.

    Article  Google Scholar 

  • Akgun A and Turk N 2010 Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis; Environ. Earth Sci. 61(3) 595–611.

    Article  Google Scholar 

  • Althuwaynee O F, Pradhan B and Lee S 2012 Application of an evidential belief function model in landslide susceptibility mapping; Comput. Geosci. 44 120–135.

    Article  Google Scholar 

  • Althuwaynee O F, Pradhan B, Park H J and Lee J H 2014 A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping; Catena 114 21–36.

    Article  Google Scholar 

  • An P, Moon W and Rencz A 1991 Application of fuzzy set theory for integration of geological, geophysical and remote sensing data; Can. J. Explor. Geophys. 27 1–11.

    Google Scholar 

  • Ayalew L, Yamagishi H and 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 73–81.

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H and 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. 81 432–445.

    Article  Google Scholar 

  • Barrile V, Cirianni F, Leonardi G and Palamara R 2016 A fuzzy-based methodology for Landslide Susceptibility Mapping; Procedia Soc. Behav. Sci. 223 896–902.

    Article  Google Scholar 

  • Caniani D, Pascale S, Sdao F and Sole A 2008 Neural networks and landslide susceptibility: A case study of the urban area of Potenza; Nat. Hazards 45 55–72.

    Article  Google Scholar 

  • Carson M A and Kirkby M J 1972 Hillslope form and process; Cambridge University Press, New York, Vol. 178(4065), pp. 1083–1084, https://doi.org/10.1126/science.178.4065.1083-a.

  • Cascini L, Cuomo S and Sala M D 2011 Spatial and temporal occurrence of rainfall-induced shallow landslides of flow type: A case of Sarno-Quindici, Italy; Geomorphology 126(1–2) 148–158, https://doi.org/10.1016/j.geomorph.2010.10.038.

    Article  Google Scholar 

  • Cevik E and Topal T 2003 GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey); Environ. Geol. 44 949–962.

    Article  Google Scholar 

  • Chang S H and Wan S 2014 Discrete rough set analysis of two different soil behavior induced landslides in National Shei-Pa Park, Taiwan; Geosci. Front., https://doi.org/10.1016/j.gsf.2013.12.010.

  • Chou W C, Lin W T and Lin C Y 2009 Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: A case study in central Taiwan; Environ. Monit. Assess. 152(1–4) 245–257.

    Article  Google Scholar 

  • Chung C and Fabbri A 2001 Prediction models for landslide hazard zonation using a fuzzy set approach; Geomorphology and Environmental Impact Assessment Balkema, Lisse, The Netherlands, pp. 31–47.

  • Chung C J F and Fabbri A G 2003 Validation of spatial prediction models for landslide hazard mapping; Nat. Hazards 30(3) 451–472.

    Article  Google Scholar 

  • Comegna L, Picarelli L, Bucchignani E and Mercogliano P 2013 Potential effects of incoming climate changes on the behavior of slow active landslides in clay; Landslides 10(4) 373–391, https://doi.org/10.1007/s10346-012-0339-3.

    Article  Google Scholar 

  • Conforti M, Pascale S, Robustelli G and Sdao F 2013 Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo river catchment (northern Calabria, Italy); Catena, https://doi.org/10.1016/j.catena.2013.08.006.

  • Crosby D A 2006 The effect of DEM resolution on the computation of hydrologically significant topographic attributes; M. S. Thesis Arts, Department of geography, College of Arts and Sciences, University of South Florida, 135p.

  • Crozier M J 2010 Deciphering the effect of climate change on landslide activity: A review; Geomorphology 124 260–267.

    Article  Google Scholar 

  • Eastman J R 2003 IDRISI Kilimanjaro: Guide to GIS and image processing; Clark Labs, Clark University, Worcester, 328p.

  • Eshghabad S M, Solaimani K and Omidvar E 2012 Landslide susceptibility mapping using multiple regression and GIS tools in Tajan Basin, north of Iran; Environ. Nat. Res. Res. 2(3).

  • Evans I S 1979 An integrated system of terrain analysis and Slope mapping; Final Report on Grant DA-ERO-591-73-G0040, University of Durham, Durham, UK.

  • Farrokhnia A, Pirasteh S, Biswajeet P, Pourkermani M and Arian M 2011 A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran: Contribution from Geo information technology; Arabian Geosci. J. 4 1337–1349.

    Article  Google Scholar 

  • Glade T and Crozier M J 2005 A review of scale dependency in landslide hazard and risk analysis; Landslide Hazard Risk, https://doi.org/10.1002/9780470012659.ch3.

  • Hong H, Xu C and Bui D T 2015 Landslide susceptibility assessment at the Xiushui area (China) using frequency ratio model; Procedia Earth Planet. Sci. 15 513–517, https://doi.org/10.1016/j.proeps.2015.08.065.

    Article  Google Scholar 

  • Ilanloo M 2011 A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran; Procedia Soc. Behav. Sci. 19 668–676.

    Article  Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi H R, Rezaeian J and Sattarian A 2014 GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran; Int. J. Environ. Sci. Technol. 11(4) 909–926.

    Article  Google Scholar 

  • Khosravi K, Nohani E, Maroufinia E and Pourghasemi H R 2016a A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique; Nat. Hazards 83(2) 1–41.

    Article  Google Scholar 

  • Khosravi K, Pourghasemi H R, Chapi K and Bahri M 2016b Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting; Environ. Monit. Assess. 188(12) 656.

    Article  Google Scholar 

  • Koukis G and Ziourkas C 1991 Slope instability phenomena in Greece: A statistical analysis; Bull. Int. Assoc. Eng. Geol. 43 47–60.

    Article  Google Scholar 

  • Lee S and Dan N T 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) 778–787.

    Article  Google Scholar 

  • Lee S and Pradhan B 2006 Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia; J. Earth Syst. Sci. 115 661–672.

    Article  Google Scholar 

  • Lee S and Sambath T 2006 Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models; Environ. Geol. 50 847–855.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lee Y F and Chi Y Y 2011 Rainfall-induced landslide risk at Lushan, Taiwan; Eng. Geol. 123 113–121, https://doi.org/10.1016/j.enggeo.2011.03.006.

    Article  Google Scholar 

  • Lefteri H T and Robert E U 1997 Fuzzy and neural approaches in engineering; A Wiley Interscience Publication.

    Google Scholar 

  • Li L, Liu R, Pirasteh S, Chen X, Long H and Li J 2017 A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping; Arab. J. Geosci. 10 209, https://doi.org/10.1007/s12517-017-3002-4.

    Article  Google Scholar 

  • Meteorological Organization of Mazandaran Province 2013 Long-term report of synoptic stations.

  • Metz C E 1978 Basic principles of ROC analysis; Semin. Nucl. Med. 8 283–298.

    Article  Google Scholar 

  • Moore I D and Burch G J 1986 Sediment transport capacity of sheet and rill flow: Application of unit stream power theory; Water Resour. Res. 22 1350–1360.

    Article  Google Scholar 

  • Moore I D and Wilson J P 1992 Length-slope factors for the revised universal soil loss equation: Simplified method of estimation; J. Soil Water Conserv. 47(5) 423–428.

    Google Scholar 

  • Moore I, Grayson R and Ladson A 1991 Digital terrain modeling: A review of hydrological, geomorphological, and biological applications; Hydrol. Process. 5 3–30.

    Article  Google Scholar 

  • Morrison A M 2005 Receiver operating characteristic (ROC) curve preparation: A tutorial; Boston, Massachusetts Water Resources Authority, Report ENQUAD 2005-20, 5p.

  • Nezhadali E, Ouri A E and Pazira E 2013 Evaluation and Landslide hazard zonation using LIM model with GIS techniques (case study: Saein watershed. Ardabil); Int. J. Farming Allied Sci., ISSN 2322–4134.

  • Ozdemir H and Turoglu H 2007 Landslide susceptibility assessment using GIS and RS in the Havran river Basin (Balikesir-Turkey); In: 12th Conference of International Association for Mathematical Geology, pp. 26–31.

  • Ozdemir A and Altural T 2013 A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey; J. Asian Earth Sci. 64(5) 180–197.

    Article  Google Scholar 

  • Park S, Choi C, Kim B and Kim J 2013 Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea; Environ. Earth Sci. 68(5) 1443–1464.

    Article  Google Scholar 

  • Pham B T, Bui D T, Pourghasemi H R, Indra P and Dholakia M B 2017 Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve Bayes, multilayer perceptron neural networks, and functional trees methods; Theor. Appl. Climatol. 1–19, https://doi.org/10.1007/s00704-015-1702-9.

  • Pirasteh S and Li J 2016 Landslides investigations from geo-informatics perspective: Quality, challenges, and recommendations; Geomatics Nat. Hazards Risk 1–18, https://doi.org/10.1080/19475705.2016.1238850.

  • Pirasteh S and Li J 2017a Probabilistic frequency ratio (PFR) model for quality improvement of landslides susceptibility mapping from LiDAR point clouds; Geoenviron. Disaster J. 4–19, https://doi.org/10.1186/s40677-017-0083-z.

  • Pirasteh S and Li J 2017b Global Changes and Natural Disaster Management: Geo-information Technologies; Springer, Berlin, ISBN 978-3-319-51843-5.

    Book  Google Scholar 

  • Pirasteh S, Jonathan L and Michael C 2017 Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros mountains, Iran; Geocarto Int. J., https://doi.org/10.1080/10106049.2017.1316779.

  • Pourghasemi H R, Pradhan B and Gokceoglu C 2012a Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran; Nat. Hazards 63(2) 965–996.

    Article  Google Scholar 

  • Pourghasemi H R, Mohammady M and Pradhan B 2012b Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran; Catena 97 71–84.

    Article  Google Scholar 

  • Pourghasemi H R, Jirandeh A G, Pradhan B, Xu C and Gokceoglu C 2013 Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran; J. Earth Syst. Sci. 122(2) 349–369.

    Article  Google Scholar 

  • Pradhan B, Oh H J and Buchroithner M 2010 Weights of evidence model applied to landslide susceptibility mapping in a tropical hilly area; Geomat. Nat. Hazards 1(3) 199–223.

    Article  Google Scholar 

  • Raghuvanshi T K, Negassa L and Kala P M 2015 GIS based grid overlay method vs. 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. 18 235–250.

    Google Scholar 

  • Rahmati O and Pourghasemi H R 2017 Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models; Water Resour. Manag. 31(5) 1473–1487.

    Article  Google Scholar 

  • Ramesh V, Phaomei T, Baskar M and Anbazhagan S 2016 Application of fuzzy gamma operator in landslide susceptibility mapping along Yercaud Ghat road section, Tamil Nadu, India; In: Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment (ed.) Raju N, Springer, https://doi.org/10.1007/978-3-319-18663-4_82.

  • Regmi A D, Devkota K C, Yoshida K, Pradhan B, Pourghasemi H R, Kumamoto T and Akgun A 2013 Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya; Arab. J. Geosci. 7(2) 725–742.

    Article  Google Scholar 

  • Renard K G, Foster G R, Weesies G A, Mccool D K and Yoder D C 1997 Predicting soil erosion by water: A guide to conservation planning with the revised soil loss equation (RUSLE); US Dept. of Agriculture, Agriculture Handbook No. 703, 404p.

  • Rosenfeld C L 1994 The geomorphological dimensions of natural disasters; Geomorphology 10(1) 27–36.

    Article  Google Scholar 

  • Ruff M and Czurda K 2008 Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria); Geomorphology 94(3–4) 314–324.

    Article  Google Scholar 

  • Safari A and Moghimi E 2009 Geomorphologic assessment of urban development and vulnerability caused by landslide in mountainous hillsides of Tehran metropolis; Phys. Geogr. Res. Q. 67 53–71.

    Google Scholar 

  • Saha A K, Gupta R P and Arora M K 2002 GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas; Int. J. Remote Sens. 23 357–369.

    Article  Google Scholar 

  • Sahin E K, Ipbuker C and Kavzoglu T 2015 A comparison of feature and expert-based weighting algorithms in landslide susceptibility mapping; Procedia Earth Planet. Sci. 15 462–467.

    Article  Google Scholar 

  • Santos J G 2013 GIS-based hazard and risk maps of the Douro river basin (north-eastern Portugal); Geomat. Nat. Hazards Risk 2 90–114.

    Google Scholar 

  • Senatore A, Mendicino G, Smiatek G and Kunstmann H 2011 Regional climate change projections and hydrological impact analysis for a Mediterranean basin in Southern Italy; J. Hydrol. 399 70–92, https://doi.org/10.1016/j.jhydrol.2010.12.035.

    Article  Google Scholar 

  • Shahabi H, Khezri S, Bin Ahmad B and Hashim M 2014 Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models; Catena 115 55–70.

    Article  Google Scholar 

  • Sujatha E R, Rajamanickam G V and Kumaravel P 2012 Landslide susceptibility analysis using probabilistic certainty factor approach: A case study on Tevankarai stream watershed, India; Environ. Earth Sci. 120(5) 1337–1350.

    Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I and Dick O B 2012 Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS; Comput. Geosci. 45 199–211.

    Article  Google Scholar 

  • Vakhshoori V and Zare M 2016 Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods; Geomat. Nat. Hazards Risk 5 1731–1752.

    Article  Google Scholar 

  • Van Westen C J 1997 Statistical landslide hazard analysis; Application guide, ILWIS 2.1 for Windows. ITC, Enschede, the Netherlands, pp. 73–84.

  • Varnes D J 1978 Slope movements: Types and processes; In: Landslide Analysis and Control, National Academy of Sciences (eds) Schuster R L and Krizek R J, Transportation Research Board Special Report, Vol. 176, pp. 11–33.

  • Wilson J P and Gallant J C 2000 Digital terrain analysis; In: Terrain Analysis: Principles and Applications (eds) Wilson J P and Gallant J C, Wiley, New York, pp. 1–27.

    Google Scholar 

  • Wu Y, Chen L, Cheng C, Yin K and Torok A 2014 GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China; Eng. Geol. 175 9–21.

    Article  Google Scholar 

  • Yalcin A 2008 GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations; Catena 72 1–12.

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu A C and 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) 274–287.

    Article  Google Scholar 

  • Ye C, Cui P, Zhang J, Li J, Meng Q, Bi X and Pirasteh S 2016 GiT-based structural geologic feature analysis of the southern segment of Longmenshan Fault Zone for earthquake evidence; J. Mountain Sci. 13(5) 906–916, https://doi.org/10.1007/s116290153796z.

    Article  Google Scholar 

  • Yilmaz I 2009 Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat Turkey); Comput. Geosci. 35 1125–1138.

    Article  Google Scholar 

  • Yusof N, Ramli M F, Pirasteh S and Shafri H Z M 2011 Landslides and lineaments mapping along the Simpang Pulai to Kg Raja highway, Malaysia; Int. J. Remote Sens. 32(14) 4089–4105.

    Article  Google Scholar 

  • Zadeh L A 1965 Fuzzy sets; IEEE Inf. Control 8 338–353.

    Article  Google Scholar 

  • Zevenbergen L W and Thorne C R 1987 Quantitative analysis of land surface topography; Earth Surf. Process. Landf. 12(1) 47–56.

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the Mazandaran Regional Water Authority and the Natural Resources Bureau of Mazandaran for providing data to accomplish this study.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran

    Mahvash Gholami

  2. Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz, Iran

    Esmaeil Nekouei Ghachkanlu

  3. Department of Watershed Management Engineering, Sari Agricultural Science and Natural Resources University, Sari, Iran

    Khabat Khosravi

  4. Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering (FGEE), Southwest Jiaotong University, Chengdu, China

    Saied Pirasteh

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  1. Mahvash Gholami
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Gholami, M., Ghachkanlu, E.N., Khosravi, K. et al. Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. J Earth Syst Sci 128, 42 (2019). https://doi.org/10.1007/s12040-018-1047-8

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