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Machine Learning‐Based Wave Model With High Spatial Resolution in Chesapeake Bay

التفاصيل البيبلوغرافية
العنوان: Machine Learning‐Based Wave Model With High Spatial Resolution in Chesapeake Bay
المؤلفون: Jian Shen, Zhengui Wang, Jiabi Du, Yinglong J. Zhang, Qubin Qin
المصدر: Earth and Space Science, Vol 11, Iss 3, Pp n/a-n/a (2024)
بيانات النشر: American Geophysical Union (AGU), 2024.
سنة النشر: 2024
المجموعة: LCC:Astronomy
LCC:Geology
مصطلحات موضوعية: wave, machine learning, data‐driven model, SCHISM, Chesapeake Bay, Astronomy, QB1-991, Geology, QE1-996.5
الوصف: Abstract A high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2333-5084
Relation: https://doaj.org/toc/2333-5084
DOI: 10.1029/2023EA003303
URL الوصول: https://doaj.org/article/8b6f21a1e40e4349a9cdb09f12e2f412
رقم الأكسشن: edsdoj.8b6f21a1e40e4349a9cdb09f12e2f412
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:23335084
DOI:10.1029/2023EA003303