A Novel Early Warning Method for Handling Non-Homogeneous Information

Zi-Xin Zhang, Liang Wang () and Ying-Ming Wang
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Zi-Xin Zhang: School of Economics and Management, Yango University, Fuzhou 350015, China
Liang Wang: Decision Sciences Institute, Fuzhou University, Fuzhou 350116, China
Ying-Ming Wang: School of Economics and Management, Yango University, Fuzhou 350015, China

Mathematics, 2022, vol. 10, issue 16, 1-20

Abstract: Early warnings are an indispensable part of emergency management, which is a powerful way to eliminate or reduce the negative impacts caused by emergencies in advance. Early warning problems have been discussed from different perspectives and have obtained fruitful results. Information plays a critical role in all kinds of decision problems, with no exception for the early warning problem. There are various information types related to emergencies in real-world situations; however, existing early warning studies only considered a single information type, which might not describe the problem properly and comprehensively. To enrich existing early warning studies, a novel early warning method considering non-homogeneous information together with experts’ hesitation is proposed, in which numerical values, interval values, linguistic terms, and hesitant fuzzy linguistic terms are considered. To facilitate the computations with non-homogeneous information, a transformation process needs to be conducted. On such a basis, a fuzzy TOPSIS method based on alpha-level sets is employed to handle the transformed fuzzy information due to its superiority in obtaining information and its capacity to contain as much information as possible during the early warning process. Additionally, two different options are provided to analyze the status and tendency of early warning objects. Finally, an illustrative example about early warnings about landslides and a related comparison are conducted to demonstrate the novelty, superiority, and feasibility and validity of the proposed method.

Keywords: non-homogeneous information; early warning; fuzzy TOPSIS method; alpha-level sets (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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