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Zeda Yin

As seen in: MDPI
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Articles

Deep Learning Models for Water Stage Predictions in South Florida

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By Jimeng Shi, Zeda Yin, Rukmangadh Sai Myana, Khandker S. Ishtiaq
| ascelibrary.org
Practical Applications Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. However, the current physics-based computational tools are based on solving complex partial differential equations and are time consuming, especially for large watersheds and for longer simulations. To overcome this problem, we aimed to train deep learning (DL) surrogate models to rapidly predict the water stages.
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Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation

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By Lili Wang, Arturo León, Linlong Bian, Zeda Yin
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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