An evidential MULTIMOORA approach to assessing disaster risk reduction education strategies under a heterogeneous linguistic environment. (August 2022) - Liu, Runze; Fei, Liguo; Mi, Jianing
Abstract: Reducing the losses caused by disasters and strengthening the construction of public security are the key measures for achieving sustainable development. The lack of public awareness of emergency preparedness poses a serious obstacle to the realization of this goal, and as a result of it, emergency management urgently needs to pay attention to disaster risk reduction (DRR) education. The selection of disaster risk reduction education strategies (DRRESs) is crucial for promoting DRR knowledge and cultivating the DRR ability of citizens. Therefore, this study proposes a comprehensive strategy evaluation method based on Dempster-Shafer theory (DST) and the MULTIMOORA method in a heterogeneous linguistic environment. To comprehensively evaluate the performance of DRRESs, an evaluation index system containing three levels of criteria is constructed, providing a powerful basis for strategy assessment. To flexibly express evaluators' cognition and judgement of strategies, this study allows evaluators to use various linguistic expressions. Furthermore, to calculation in the evaluation process convenient, different linguistic representations are unified as the basic probability assignment (BPA) in DST, and the effective aggregation of multi-expert information is realized based on the idea of evidence fusion. The MULTIMOORA method is introduced into the DST framework, and an evidential MULTIMOORA approach is proposed to realize the multi-dimensional evaluation of DRRESs from different perspectives. Finally, an empirical analysis of the proposed evaluation method is conducted with a practical DRRES selection problem as the case. The consistency of the evaluation results with the real selection illustrates the validity of this study. Furthermore, several management suggestions are proposed to improve the formulation and implementation of DRRESs through in-depth analysis of the evaluation results.