An Intelligent Decision Support Model for Emergency Management in the Fog Computing Environment

Numerous emergencies have happened recently on a worldwide scale, necessitating and occasionally requiring relevant decision-makers to focus excessively on the decision-making process. Due to the fact that decision support systems (DSS) combine data and knowledge from many domains and sources, analyze them, and store the results in the cloud to give the right users useful information to aid in decision-making, data is regarded as a core component in the design of DSS. Dealing with such enormous volumes of data results in increased mistakes, packet loss, and a high likelihood of data congestion, which is a drawback of traditional methods of storing massive amounts of data in the cloud. Therefore, before transferring data to decision support apps or sending it again to the cloud, processing, initializing, and preparing it in the fog computing environment is a necessary and appropriate option. An intelligent model for decision support applications in a fog computing environment was put out in this study. Expert review and focus group talks were employed as validation procedures in terms of relevancy and understanding of the proposed model. The observed results support the relevance and understanding of the proposed model's elements. A prototype based on the suggested model was developed to verify its validity, and its usability was then evaluated. The gathered data also supported the prototype's usefulness and decision-supporting abilities.