Bipolar intuitionistic fuzzy graph based decision-making model to identify flood vulnerable region.

Journal: Environmental science and pollution research international
Published Date:

Abstract

Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam ([Formula: see text]) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.

Authors

  • Deva Nithyanandham
    Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology (Chennai Campus), Chennai, Tamil Nadu, India.
  • Felix Augustin
    Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology (Chennai Campus), Chennai, Tamil Nadu, India.
  • Samayan Narayanamoorthy
    Department of Mathematics, Bharathiar University, Coimbatore-46, India.
  • Ali Ahmadian
    Institute of Industry Revolution 4.0, National University of Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
  • Dumitru Balaenu
    Department of Mathematics, Cankaya University, Ankara, 06530, Balgat, Turkey.
  • Daekook Kang
    Department of Industrial and Management Engineering, Inje University, Gimhae-si, Gyeongsangnam-do, Republic of Korea.