Fuzzy logic based decision support system for mass evacuation of cities prone to coastal or river flood
type: Dataset
description: The increasing risk of river flooding or coastal submersion is already visible through recent events like the storm Xynthia and the floods in the Var department, which caused several dozens of deaths in France. These catastrophic events, even if their extent remains relatively limited, would have justified a preventive evacuation of high risk prone areas. However, the consequences for the population would be much more serious when large cities of hundreds of thousands of people will be partially or totally threatened by floods. This possibility is already an actual danger for large megacities like Bangkok and Alexandria, and also threatens French cities like Tours, Paris or Nice. Being more and more aware of this possibility, big coastal, estuarine and river cities in France, in Europe and in all continents are incited to prepare emergency and mass evacuation plans in order to prevent and cope with exceptional events. The elaboration of these plans is extremely complex and difficult due to technical, organizational, sociological and even political aspects. The great majority of cities in the world prone to large scale disasters do not already have this kind of plan at their disposal. Moreover, the existing state of the art shows that there are few operational tools to help territorial managers implement these plans in the phases of preparation and crisis management. Our work aims to contribute to the development of a support method for the evacuation decision taken in a crisis management context. This method is partly based on the information included in the provisional evacuation plans produced in the preparation phase. To reach this objective, we propose to adapt the tools of the fuzzy logic approach and apply them to a set of synthesized indicators. These indicators or decision criteria have been first selected from a method of evacuation planning previously developed by the research team Avenues-GSU. These criteria integrate classic data on the hazard level (ove…
landing page: https://theses.hal.science/tel-00858055
identifier: NNT: 2013COMP2070
keyword: analyse-multicriteres-decision
keyword: catastrophes-climatiques
keyword: changement-climatique
keyword: decision-support
keyword: elevation-du-niveau-de-la-mer
keyword: evacuation-de-masse
keyword: flooding
keyword: fuzzy-logic
keyword: geographic-information-system
keyword: infoeu-reposemanticsdoctoralthesis
keyword: sad
keyword: scenarios-prospectifs
keyword: sig
keyword: spimecaengineering-sciences-physicsmechanics-physicsmed-ph
keyword: theses
issued: 2026-05-09T21:19:59.500853
modified: 2026-05-09T21:19:59.500857
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Fuzzy logic based decision support system for mass evacuation of cities prone to coastal or river flood
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00858055
issued: 2026-05-09T21:19:59.502392
modified: 2026-05-09T21:19:59.484384