| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560633 |
| ISSN: | 1405-5546 |
| Autores: | Caylor, Justine P.1 Hammell II, Robert J.1 |
| Instituciones: | 1Towson University, Department of Computer and Information Sciences, Towson, Maryland. Estados Unidos |
| Año: | 2021 |
| Periodo: | Oct-Dic |
| Volumen: | 25 |
| Número: | 4 |
| Paginación: | 863-872 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | When emergencies or disasters strike, decision-making is a critical component in emergency management. One area of emergency management is ensuring that vulnerable communities are identified and can get the aid they need before, during, and after emergency events. Artificial Intelligence (AI) can be leveraged to improve decision-making in dynamic and complex situations. We propose that Multi-Criteria Decision-Making (MCDM), specifically a hybrid methodology of AHP-TOPSIS, is an approach that can be utilized in AI that can help evaluate, prioritize, and select the most favorable alternative based on computation of the criteria. A study was conducted considering the positive COVID-19 cases in randomly selected counties in three states – Texas, California, and Oklahoma – that have historically experienced the most declared emergencies. The empirical results from the three cases (one case for each state) demonstrate the superiority of the AHP-TOPSIS approach. |
| Keyword: | Multi-criteria decision-making, Emergency management, Artificial intelligence, Social vulnerability index, AHP, TOPSIS |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
