Title

FUZZY TOPSIS APPROACH TO IDENTIFY
THE FLOOD VULNERABILITY REGION
IN SOUTH CHENNAI

Authors

A. Selvaraj, Saroj Kumar Dash,
N. Punithavelan, A. Felix
School of Advanced Sciences
Mathematics Division
VIT University, Chennai, 600127, INDIA
School of Advanced Sciences
Physics Division
VIT University
Chennai, 600127, INDIA

Abstract

The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known and very simple ranking method for solving Multi Attribute Decision Making (MADM). In many real life situations, the decision data of human judgments are often vague so that the conventional ways of using crisp numbers are inadequate. Also, using fuzzy numbers such as triangular, trapezoidal, etc. are not suitable when the uncertainties arise at six different points. Therefore, Hexagonal fuzzy number and its arithmetic operations, linguistic values are used to extend the TOPSIS method to analyze the flood vulnerability region in south Chennai.

History

Received: 2017-10-17
Revised: 2017-11-29
Published: April 15, 2018

AMS Classification, Key Words

AMS Subject Classification: 03B52, 94D05
Key Words and Phrases: linguistic variable, fuzzy sets and fuzzy logic, fuzzy-TOPSIS, hexagonal fuzzy number

Download Section

Download paper from here.
You will need Adobe Acrobat reader. For more information and free download of the reader, see the Adobe Acrobat website.

Bibliography

1

C.T.Chen, Lin, C.T., & Huang, S. F, A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102, (2006), 289- 301.

1

A.V.Devadoss, M. Syed Ismail, & A. Felix, Decagonal Fuzzy TOPSIS technique and its Application, Global Journal of Pure and Applied Mathematics, 12, (2016), 3502-506.

1

Z.P.Fan & Y.Liu. A method for Group Decision-Making based on Multi-Granularity uncertain Linguistic Information, Expert Syst Appl, 37(5), (2010), 4000-4008.

1

A. Felix, S. Christopher, & A.V.Devadoss, Extension of TOPSIS technique using the Nonagonal Fuzzy Number in an uncertain environment, Global Journal of Pure and Applied Mathematics, 12(3), 474-478, 2016.

1

D.H.Hong, Fuzzy measures for a correlation coefficient of fuzzy numbers under TW (the weakest t-norm)-based fuzzy arithmetic operations, Information Sciences, 176, (2006) 150-160.

1

Y.S.Huang, & Li, W. H. A study on aggregation of TOPSIS ideal solutions for group decision-making. Group Decision and Negotiation, (2010).

1

C.L.Hwang, & Yoon, K. , Multiple attributes decision making methods and applications. Springer, Berlin, (1981).

1

Jejal Reddy Bathi & Himangshu S. Das , Vulnerability of Coastal Communities from Storm Surge and Flood Disasters, 13(239), (2016), 1-12.

1

W. Jiang et.al, Risk assessment and validation of flood disaster based on fuzzy mathematics, Progress in Natural Science, 19, (2009) 1419–1425

1

W.B.Lee, Lau, H., Liu, Z., & Tam, S. A fuzzy analytic hierarchy process approach in modular product design. Expert Systems, 18(1), (2001), 32–42.

1

M.C.Lin, Wang, C.C., Chen, M.S., & Chang C. A. , Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17-31.

1

P.Rajarajeshwari, A.S. Sudha & R. Karthika, A new Operation on Hexagonal Fuzzy Number, International Journal of Fuzzy Logic Systems, 3(3), (2013), 15-26.

1

RJ.Tkach & Somonovic,SP, A new approach to multi-criteria decision making in water resources, Journal of Geographic Information and Decision Analysis, 1(1), (1997), 25-44.

1

T.Walczykiewicz, MultiCriteria Analysis for Selection of Activity Options Limiting Flood Risk. 42(1), (2015), 124–132

1

T.C.Wang & Chang, T.H. Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), (2007), 870-880.

1

Y.J.Wang & Lee, H.S, Generalizing TOPSIS for fuzzy multiple-criteria group decision making. Computers and Mathematics with Applications, 53(11), (2007), 1762-1772.

2

L.A.Zadeh, Fuzzy sets. Information Control 8(3),(1965),338–353.

3

Zadeh LA, The concept of a linguistic variable and its application to approximate reasoning (Part II). Information Science, 8, (1975),301–357.

How to Cite?

DOI: 10.12732/ijpam.v118i3.14 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2018
Volume: 118
Issue: 3
Pages: 667 - 674

Google Scholar; DOI (International DOI Foundation); WorldCAT.

This work is licensed under the Creative Commons Attribution International License (CC BY).

---