A spatial-temporal deep learning-based warning system against flooding hazards with an empirical study in Taiwan-Reference-Cited by-同舟云学术

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A spatial-temporal deep learning-based warning system against flooding hazards with an empirical study in Taiwan

Published:2024-01 Issue: Volume: Page:104263
ISSN:2212-4209
Container-title:International Journal of Disaster Risk Reduction
language:en
Short-container-title:International Journal of Disaster Risk Reduction

Author:

Chang Kuo-Hao^ORCID,Chiu Yun-Ting,Su Wen-Ray,Yu Yi-Chiang,Chang Chih-Hsin

Publisher

Elsevier BV

Reference36 articles.

1. Climate change 2021: the physical science basis;Masson-Delmotte,2021

2. Urban design principles for flood resilience: Learning from the ecological wisdom of living with floods in the Vietnamese Mekong Delta;Liao;Landsc. Urban Plan.,2016

3. Scenario-based stochastic shelter location-allocation problem with vulnerabilities for disaster relief network design;Hansuwa;Eur. J. Ind. Eng.,2022

4. A stochastic mixed-integer model to support foodbank resources prepositioning during the prelude to a natural disaster;Rivera;Eur. J. Ind. Eng.,2023

5. Wireless sensor network for AI-based flood disaster detection;Al Qundus;Ann. Oper. Res.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Modeling river flow for flood forecasting: A case study on the Ter river;Applied Computing and Geosciences;2024-09

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