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Yin-Hsuen Chen

As seen in: Wiley Online Library, PLOS ONE, MDPI
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Articles

Redefining Bankfull Stage Using Machine Learning Prediction in a Large Lowland River

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By Joann Mossa, Yin-Hsuen Chen, Amobichukwu C. Amanambu, Geospatial Sensing Laboratory
| Wiley Online Library Verified
CONFLICTS OF INTEREST The authors declare no conflicts of interest. DATA AVAILABILITY STATEMENT Data sharing not applicable to this article as no datasets were generated or analysed during the current study. REFERENCES (2023) Drought, Dam and Dredging Impacts on the Upper Apalachicola River and Floodplain. Doctoral dissertation, University of Florida. & (2024) Machine Learning Insights of Anthropogenic and Natural Influences on Riverbed Deformation in a Large Lowland River.
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Drone-based medication delivery for rural, flood-prone coastal communities

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By Yin-Hsuen Chen, Kevin J. O’brien, Amro M. El-Adle
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Chen Y-H, El-Adle AM, O’Brien KJ, Wentworth T, Richter HG (2025) Drone-based medication delivery for rural, flood-prone coastal communities. PLoS One 20(10): e0333696. https://doi.org/10.1371/journal.pone.0333696 Editor: Zeashan Hameed Khan, King Fahd University of Petroleum & Minerals, SAUDI ARABIA Received: April 17, 2025; Accepted: September 17, 2025; Published: October 6, 2025 Copyright: © 2025 Chen et al.
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Landslide at the River’s Edge: Alum Bluff, Apalachicola River, Florida

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By Joann Mossa, Yin-Hsuen Chen
| 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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