Skip to main content

Advertisement

SpringerLink
Log in

Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

In this paper, an adaptive neuro-fuzzy modeling (ANFIS) is applied in order to map landslide susceptibility for a Mediterranean catchment (Peloponnese, Greece). The relationship between landslides and factors influencing their occurrence is investigated in GIS environment. Seven conditioning factors, including elevation, slope angle, profile curvature, stream density, distance to main roads, geology, and vegetation were considered in the analysis. Six ANFIS models with different membership functions were developed to generate the corresponding landslide susceptibility maps. The outputs, representing the probability level of landslide occurrence, were grouped into five classes. They were then evaluated using an independent dataset of landslide events in two different validation methods: receiver operating characteristics (ROC) analysis and success and prediction rates. The majority of the calculated area under the curve values for the two validation methods was in the range 0.70–0.90 indicating between fair and very good prediction accuracy for the six models. These values also showed that the prediction accuracy depends on the membership functions examined in the ANFIS modeling. Among these functions, the difference of two sigmoidally shaped (Dsigmf) and product of two sigmoidally shaped (Psigmf) presented the highest prediction accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

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):93–106

    Article  Google Scholar 

  • Allahverdi N, Tunali A, Işik H, Kahramanli H (2011) A Takagi-Sugeno type neuro-fuzzy network for determining child anemia. Expert Syst Appl 38(6):7415–7418

    Article  Google Scholar 

  • Atsalakis GS, Valavanis KP (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36(7):10696–10707

    Article  Google Scholar 

  • Avtar R, Singh CK, Singh G, Verma RL, Mukherjee S, Sawada H (2011) Landslide susceptibility zonation study using remote sensing and GIS technology in the Ken-Betwa River Link area, India. Bull Eng Geol Env 70:595–606

    Article  Google Scholar 

  • Brabb EE (1984) Innovative approaches to landslide hazard mapping. In: Proceedings of the 4th international symposium on landslides, pp 307–324

  • Cervi F, Berti M, Borgatti L, Ronchetti F, Manenti F, Corsini A (2010) Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy). Landslides 7(4):433–444

    Article  Google Scholar 

  • Chalkias C, Ferentinou M, Polykretis C (2014a) GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4:176–190

    Article  Google Scholar 

  • Chalkias C, Ferentinou M, Polykretis C (2014b) GIS supported landslide susceptibility modeling at regional scale: an expert-based fuzzy weighting method. ISPRS Int J Geo Inf 3:523–539

    Article  Google Scholar 

  • Chalkias C, Kalogirou S, Ferentinou M (2014c) Landslide susceptibility, Peloponnese Peninsula in South Greece. J Maps 10(2):211–222

    Article  Google Scholar 

  • Chen CH, Ke CC, Wang CL (2009) A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan. Environ Geol 57:723–733

    Article  Google Scholar 

  • Chiu SL (1997) An efficient method for extracting fuzzy classification rules from high dimensional data. Adv Comput Intell 1:1–7

    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 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation, special report. National Academy Press, Washington, pp 36–75

    Google Scholar 

  • Dagdelenler G, Nefeslioglu HA, Gokceoglu C (2016) Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bull Eng Geol Env 75(2):575–590

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Ferentinou M, Chalkias C (2013) Mapping mass movement susceptibility across Greece with GIS, ANN and statistical methods. Landslide Science and Practice, pp 321–327

  • Garson GD (1991) Interpreting neural network connection weights. AI expert 6:47–51

    Google Scholar 

  • Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2016) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 1–10. doi:10.1007/s00521-016-2746-1

  • Hellenic Military Geographical Service – HMGS (1989) General Use Map of Greece, 1:50,000, Evinohorion, Nafpaktos, Amygdalea, Patrae, Halandritsa, Aiyion, Dhervenion, Xylokastro, Perahora, Kertezi, Dhafni, Kandhila, Nemea, Korinthos, Sofikon, Tripolis, Nafplion, and Lygourion sheets

  • Institute of Geology and Mineral Exploration – IGME (1993) Geological Map of Greece, 1:50,000, Evinohorion, Nafpaktos, Amygdalea, Patrae, Halandritsa, Aiyion, Dhervenion, Xylokastro, Perahora, Kertezi, Dhafni, Kandhila, Nemea, Korinthos, Sofikon, Tripolis, Nafplion, and Lygourion sheets

  • Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 13(2):379–397

    Article  Google Scholar 

  • Intarawichian N, Dasananda S (2011) Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environ Earth Sci 64:2271–2285

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Kouli M, Loupasakis C, Soupios P, Rozos D, Vallianatos F (2014) Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece. Environ Earth Sci 72(12):5197–5219

    Article  Google Scholar 

  • Laboratory of Engineering Geology, Department of Geology, University of Patras, (2016) Landslide Inventory Database. http://www.geoarch.gr/. Accessed 5 Sept 2016

  • Listo FDLR, Carvalho Vieira B (2012) Mapping of risk and susceptibility of shallow-landslide in the city of Sao Paulo, Brazil. Geomorphology 169–170:30–44

    Article  Google Scholar 

  • Meng Q, Miao F, Zhen J, Wang X, Wang A, Peng Y, Fan Q (2016) GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bull Eng Geol Environ 75(3):923–944

    Article  Google Scholar 

  • Montrasio L, Valentino R, Losi GL (2011) Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale. Nat Hazards Earth Syst Sci 11:1927–1947

    Article  Google Scholar 

  • Nazari-Shirkouhi S, Keramati A, Rezaie K (2013) Improvement of customers’ satisfaction with new product design using an adaptive neuro-fuzzy inference systems approach. Neural Comput Appl 23(1):333–343

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37:1264–1276

    Article  Google Scholar 

  • OpenStreetMap (2016) https://www.openstreetmap.org/. Accessed 15 Sept 2016

  • Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30

    Article  Google Scholar 

  • Palamakumbure D, Flentje P, Stirling D (2015) Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Comput Geosci 82:13–22

    Article  Google Scholar 

  • Papathanassiou G, Valkaniotis S, Ganas A, Pavlides S (2013) GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands. Greece. Landslides 10(6):771–783

    Article  Google Scholar 

  • Peduzzi P (2010) Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach. Nat Hazards Earth Syst Sci 10:623–640

    Article  Google Scholar 

  • Petković D, Ćojbašić Ž (2012) Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability. Neural Comput Appl 21(8):2065–2070

    Article  Google Scholar 

  • Pnevmatikos JD, Katsoulis BD (2006) The changing rainfall regime in Greece and its impact on climatological means. Meteorol Appl 13:331–345

    Article  Google Scholar 

  • Polykretis C, Ferentinou M, Chalkias C (2015) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ 74:27–45

    Article  Google Scholar 

  • Pradhan AMS, Kim YT (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Nat Hazards 72:1189–1217

    Article  Google Scholar 

  • Pradhan AMS, Kim YT (2015) Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events. Environ Earth Sci 73:5761–5771

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235

    Article  Google Scholar 

  • Reis S, Yalcin A, Atasoy M, Nisanci R, Bayrak T, Erduran M, Sancar C, Ekercin S (2012) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ Earth Sci 66:2063–2073

    Article  Google Scholar 

  • Remondo J, González-Díez A, Díaz de Terán JR, Cendrero A (2003) Landslide susceptibility models utilizing spatial data analysis techniques: a case study from the Lower Deba Valley, Guipúzcoa (Spain). Nat Hazards 30(3):267–279

    Article  Google Scholar 

  • Rezaeianzadeh M, Tabari H, Arabi Yazdi A, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25(1):25–37

    Article  Google Scholar 

  • Romer C, Ferentinou M (2016) Shallow landslide susceptibility assessment in a semiarid environment—a quaternary catchment of KwaZulu-Natal, South Africa. Eng Geol 201:29–44

    Article  Google Scholar 

  • Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Nat Hazards 65(1):523–543

    Article  Google Scholar 

  • Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 109:108–121

    Article  Google Scholar 

  • Sakkas G, Misailidis I, Sakellariou N, Kouskouna V, Kaviris G (2016) Modeling landslide susceptibility in Greece: a weighted linear combination approach using analytic hierarchical process, validated with spatial and statistical analysis. Nat Hazards 84(3):1873–1904

    Article  Google Scholar 

  • Sdao F, Lioi DS, Pascale S, Caniani D, Mancini IM (2013) Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat Hazards Earth Syst Sci 13:395–407

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219

    Article  Google Scholar 

  • Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506

    Article  Google Scholar 

  • Special Secretariat for Water (2012) Management Plan for the River Catchments of Drainage District of Northern Peloponnese. Ministry of Environment, Energy and Climate Change, Athens, pp 1–456

    Google Scholar 

  • Su C, Wang L, Wang X, Huang Z, Zhang X (2015) Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards 76:1759–1779

    Article  Google Scholar 

  • Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, pp 55–60

  • Talei A, Chua LHC, Quek C (2010) A novel application of a neuro-fuzzy computational technique in event-based rainfall–runoff modeling. Expert Syst Appl 37(12):7456–7468

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (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 

  • Tsagas D (2011) Geomorphological observations, and gravity movements in northern Peloponnesus. PhD dissertation, National and Kapodistrian University of Athens

  • Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74(3):1489–1516

    Article  Google Scholar 

  • Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides 13(2):305–320

    Article  Google Scholar 

  • Uçar T, Karahoca A, Karahoca D (2013) Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Comput Appl 23(2):471–483

    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 

Download references

Acknowledgements

The authors are grateful to the anonymous reviewers for their useful comments and constructive manuscript revision.

Author information

Authors and Affiliations

  1. Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece

    Christos Polykretis & Christos Chalkias

  2. Department of Civil Engineering Science, University of Johannesburg, Auckland Park Kingsway Campus, Auckland, 2006, South Africa

    Maria Ferentinou

Authors
  1. Christos Polykretis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christos Chalkias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Ferentinou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Polykretis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polykretis, C., Chalkias, C. & Ferentinou, M. Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bull Eng Geol Environ 78, 1173–1187 (2019). https://doi.org/10.1007/s10064-017-1125-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-017-1125-1

Keywords

Search

Navigation