A New Approach Using Hedge Algebra Combined with Kansei Evaluation for Intelligent Disaster Decision Support and Its Application

Abstract

Research into finding the the optimal path(s) for autonomous vehicle services in the traversing of disaster has gained traction both nationally and internationally. In disaster response services in cities, there are limited ways to enforce priorities for emergency service vehicles, the focus being on the identification of the best optimal routes to provide disaster control services in disaster zones which are characterised by inherent uncertainty. Autonomous decisions become significant to quantify linguistics used to make the optimal decision for controlling emergency service vehicles for the problems in disaster zones. In this paper, we present a new approach which implements autonomous emergency service vehicle control using hedge algebras and Kansei evaluation in decision-making to enforce optimal service vehicle control in cities. The proposed model is designed to quantify qualitative factors together with human sensibilities in decision-making using Kansei evaluation together with preferences in decision support. The experimental results show that the posited approach achieves a significant improvement for optimal decision-making in real-time emergency service vehicle control in disaster situations.