Skip to main content

Advertisement

SpringerLink
Log in

Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

One of the most important natural hazards is landslides that after the earthquake and floods cause the highest damage to humans. Nowadays, landslide events are taken into consideration by the experts in this domain and their mapping and detecting always is one of the important concerns. One of the useful methods is multi-criteria decision making that is used in order to prioritize landslide-prone zones. These methods perform based on several quantitative or qualitative criteria. Multi-criteria decision-making methods that have been the focus of researchers in recent decades use several measurement criteria instead of one. However, due to the broadness and diversity of existing methods the selection of a proper approach has become cumbersome; therefore, selecting a reliable technique for landslide evaluation can be of a great interest. In this study MCDM methods (VIKOR, PROMETHEE II, and permutation) were applied in order to detect and map landslide-prone points. Then obtained results were assessed by using prediction area (P–A) technique. Consequently, the intersection points of the P–A plot for PROMETHEE II model show 82 percent of landslide occurrences predicted only in 18 percent of the study area, while the intersection point of the P–A plot for permutation and VIKOR models shows 67 and 73 percent of landslide occurrences predicted only in 33 and 27 percent of the study area, respectively.

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
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Ameri AA, Pourghasemi HR, Cerda A (2018) Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: a comparison among TOPSIS, VIKOR, SAW, and CF multi criteria decision making models. Sci Total Environ 613:1385–1400

    Article  Google Scholar 

  • Ameri AA, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J (2019) GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci Total Environ 658:160–177

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bălteanu D, Chendeş V, Sima M, Enciu P (2010) A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology 124(3–4):102–112

    Article  Google Scholar 

  • Bashiri M, Koosha M, Karimi H (2012) Permutation based decision making under fuzzy environment using Tabu search. Int J Ind Eng Comput 3(3):301–312

    Google Scholar 

  • Bednarik M, Yilmaz I, Marschalko M (2012) Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’ landslide area in south-west Slovakia. Nat Hazards 64(1):547–575

    Article  Google Scholar 

  • Brans JP, Vincke P, Mareschal B (1986) How to select and how to rank projects: the PROMETHEE method. Eur J Oper Res 24(2):228–238

    Article  Google Scholar 

  • Caravaggio N, Caravella S, Ishizaka A, Resce G (2019) Beyond CO2: a multi-criteria analysis of air pollution in Europe. J Clean Prod 219:576–586

    Article  Google Scholar 

  • Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB (2019) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. CATENA 172:212–231

    Article  Google Scholar 

  • Drazba MC, Yan-Richards A, Wilkinson S (2018) Landslide hazards in Fiji, managing the risk and not the disaster, a literature review. Proc Eng 212:1334–1338

    Article  Google Scholar 

  • Espizua LE, Bengochea JD (2002) Landslide hazard and risk zonation mapping in the Rio Grande Basin, Central Andes of Mendoza, Argentina. Mt Res Dev 22(2):177–185

    Article  Google Scholar 

  • Firomsa M, Abay A (2019) Landslide assessment and susceptibility zonation in Ebantu district of Oromia region, western Ethiopia. Bull Eng Geol Environ 78(6):4229–4239

    Article  Google Scholar 

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019) An improved data-driven multiple criteria decision-making procedure for spatial modeling of mineral prospectivity: adaption of prediction–area plot and logistic functions. Nat Resour Res 28(4):1299–1316

    Article  Google Scholar 

  • Gupta V, Bhasin RK, Kaynia AM, Tandon RS, Venkateshwarlu B (2016) Landslide hazard in the Nainital township, Kumaun Himalaya, India: the case of September 2014 Balia Nala landslide. Nat Hazards 80(2):863–877

    Article  Google Scholar 

  • Hoseinzade Z, Mokhtari AR, Zekri H (2018) Application of radial basis function in the analysis of irregular geochemical patterns through spectrum-area method. J Geochem Explor 194:257–265

    Article  Google Scholar 

  • Huang JJ, Tzeng GH, Liu HH (2009) A revised VIKOR model for multiple criteria decision making - the perspective of regret theory. In: Shi Y, Wang S, Peng Y, Li J, Zeng Y (eds) Cutting-edge research topics on multiple criteria decision making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer. Berlin, Heidelberg, pp 761–768

    Google Scholar 

  • Jam AS, Mosaffaie J, Sarfaraz F, Shadfar S, Akhtari R (2021) GIS-based landslide susceptibility mapping using hybrid MCDM models. Nat Hazards. https://doi.org/10.1007/s11069-021-04718-5

    Article  Google Scholar 

  • Lee S, Lee MJ, Lee S (2018) Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environ Earth Sci 77(18):656

    Article  Google Scholar 

  • Lin W, Yin K, Wang N, Xu Y, Guo Z, Li Y (2021) Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China. Nat Hazards 106(1):679–700

    Article  Google Scholar 

  • Mareschal  B, Brans  J P, & Vincke P (1984) PROMETHEE: A new family of outranking methods in multicriteria analysis (No. 2013/9305). ULB--Universite Libre de Bruxelles

  • Mistri P, Sengupta S (2019) Multi-criteria decision-making approaches to agricultural land suitability classification of Malda District, Eastern India. Nat Resour Res 29:1–20

    Google Scholar 

  • Mokhtari M, Hoseinzade Z, Shirani K (2020) A comparison study on landslide prediction through FAHP and Dempster–Shafer methods and their evaluation by P–A plots. Environ Earth Sci 79(3):76

    Article  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(3–4):329–341

    Article  Google Scholar 

  • Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using weights-of-evidence modeling technique. Nat Resour Res 16(2):121–134

    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):77–95

    Article  Google Scholar 

  • Salleh SA, Rahman AAA, Othman AN, Mohd WNW (2018) Comparative study of landslides susceptibility mapping methods: multi-criteria decision making (MCDM) and artificial neural network (ANN). IOP Conf Ser Earth Environ Sci 117(1):012035

    Article  Google Scholar 

  • Sun HY, Wu X, Wang DF, Liang X, Shang YQ (2019) Analysis of deformation mechanism of landslide in complex geological conditions. Bull Eng Geol Environ 78(6):4311–4323

    Article  Google Scholar 

  • Triantaphyllou E (2000) Multi-criteria decision making methods. In: Multi-criteria decision making methods: a comparative study. Applied optimization, vol 44. Springer, Boston, MA, pp 5–21

  • Xiong G, Ruan Y, Yang J (2005) Analysis on relation between rainfall and slope stability. Undergr Sp 1(7):1017–1020

    Google Scholar 

  • Yousefi M, Carranza EJM (2015) Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Comput Geosci 79:69–81

    Article  Google Scholar 

  • Yousefi M, Nykänen V (2016) Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. J Geochem Explor 164:94–106

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geosci Front 12(2):639–655

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

    Zohre Hoseinzade & Asal Zavarei

  2. Soil Conservation and Watershed Management Research Department, Isfahan, Iran

    Kourosh Shirani

  3. Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran

    Kourosh Shirani

Authors
  1. Zohre Hoseinzade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asal Zavarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kourosh Shirani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the evaluation of the processing and selecting the final method; all authors cross-checked the contents and approved the final version of the manuscript.

Corresponding author

Correspondence to Zohre Hoseinzade.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hoseinzade, Z., Zavarei, A. & Shirani, K. Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation. Nat Hazards 109, 2489–2507 (2021). https://doi.org/10.1007/s11069-021-04929-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-021-04929-w

Keywords

Search

Navigation