An attribute-based model to retrieve storm surge disaster cases

Title

An attribute-based model to retrieve storm surge disaster cases

 Author

Wang, Ke (Tsinghua University)
Reniers, G.L.L.M.E. (TU Delft Safety and Security Science; Katholieke Universiteit Leuven; Universiteit Antwerpen) ORCID 0000-0001-9592-1811
Yang, Yongsheng (Tsinghua University)
Li, Jian (Tsinghua University)
Huang, Quanyi (Tsinghua University)

Contributor

Adrot, Anouck (editor)
Grace, Rob (editor)
Moore, Kathleen (editor)
Zobel, Christopher W. (editor)

Date

2021

 Abstract

In China, storm surge disasters cause severe damages in coastal regions. One of the most critical tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model, and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides valuable information for the government to make real-time response plans.

 Subject

Affected region prediction
Multiple attributes
Retrieval model
Storm surge disaster

 To reference this document use:

http://resolver.tudelft.nl/uuid:f59b5b31-f890-4d1f-9a2f-aa77bca15333

 Publisher

Information Systems for Crisis Response and Management, ISCRAM

 ISBN

9781949373615

 Source

ISCRAM 2021 - Proceedings: 18th International Conference on Information Systems for Crisis Response and Management

 Event

18th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2021, 2021-05-23 → 2021-05-26, Blacksburg, United States

 Series

Proceedings of the International ISCRAM Conference, 2411-3387, 2021-May

 Part of collection

Institutional Repository

 Document type

conference paper

 Rights

© 2021 Ke Wang, G.L.L.M.E. Reniers, Yongsheng Yang, Jian Li, Quanyi Huang
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