A hybrid model based on PROMETHEE and PLTSs for the assessment of public participation in community meteorological disaster prevention and mitigation

Article type: Research Article

Authors: Li, Chenliang^a | Yu, Xiaobing^{a; b; *}

Affiliations: [a] School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China | [b] Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China

Correspondence: [*] Corresponding author. Xiaobing Yu, E-mail: yuxb111@163.com.

Abstract: Communities are the fundamental units of society, and community-based disaster management is the foundation of societal disaster management systems. It is important to implement disaster prevention and mobilize all residents in the community to participate in preparedness activities. However, people’s attitudes and understanding of these issues are often ambiguous because meteorological disaster prevention and mitigation (MDPM) is complex. A hybrid model based on probabilistic term sets (PLTSs) and PROMETHEE method is put forward to solve this problem. To solve the problem from the view of big data, the experimental data are from Baidu’s disaster prevention and mitigation questionnaires. The data of these questionnaires are aggregated through PLTSs. Then, the PROMETHEE method is used to learn about the public’s understanding of community meteorological disaster prevention and mitigation (CMDPM) information and their willingness to participate in activities. The results indicate that communities in East, Northwest, Southwest, and North China have a higher willingness to join volunteer services. The proposed model makes it more convenient for decision-makers (DMs) to describe problems by PLTSs and is more appropriate for individuals’ understanding and communication.

Keywords: Meteorological disaster prevention and mitigation, PROMETHEE method, community-based disaster management

DOI: 10.3233/JIFS-202026

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9583-9595, 2021