A multi-agent-based model for sustainable governance of urban flood risk mitigation measures

Koç K., Işık Z.

Natural Hazards, vol.104, no.1, pp.1079-1110, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 104 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1007/s11069-020-04205-3
  • Journal Name: Natural Hazards
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, PAIS International, Pollution Abstracts, Sociological abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.1079-1110
  • Keywords: Urban flood, Risk mitigation measures, Flood risk factors, Flood risk governance, Multi-agent-based model, RESILIENCE INDEX, DECISION-MAKING, MANAGEMENT, SYSTEM, VULNERABILITY, BUILDINGS, PROVINCE, VIEWS
  • Yıldız Technical University Affiliated: Yes


Consequences of urban foods increased and diversifed in terms of social, economical and environmental efects, due to the dense and unplanned urbanization in areas at risk of fooding. Reducing the potential damage of food is one of the most efective and sustainable reduction strategies of food risks through adopting adequate urban food risk mitigation measures (UFRM). The main objective of this study is to ascertain urban foods and develop a model identifying UFRM in accordance with their importance in reducing the efects of foods by employing the concept of multi-agent systems. Social, economical and environmental-based agents were modelled in the agent environment to consider the three dimensions of sustainability in the proposed model. Thirteen negotiation strategies were developed for the agents to negotiate with each other. An illustrative case study was then performed to test the proposed model features, and the results were thoroughly analysed. Sensitivity analysis was conducted to specify the most sensitive strategies over the changing fuzziness level of agents. It was observed that the best strategy difers with respect to short-, medium- and long-term gains signifcantly, which, in turn, lead to alterations in the list of UFRM. This could provide diferent risk mitigation plans for diferent regions with their changing resources. In summary, this study provides an innovative multi-agent-based model that can be performed to specify the most urgent UFRM which can be used by disaster coordination and management authorities as a decision-making input.