کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8885908 | 1627244 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts
ترجمه فارسی عنوان
یک چارچوب یکپارچه که ترکیبی از یادگیری ماشین و مدل های عددی برای پیش بینی شرایط موج می
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
اقیانوس شناسی
چکیده انگلیسی
This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions together with a machine-learning algorithm that combines forecasts from multiple, independent models into a single “best-estimate” prediction of the true state. The Simulating WAves Nearshore (SWAN) physics-based model is used to compute wind-augmented waves. Ensembles are developed based on multiple simulations perturbing data input to the model. A learning-aggregation technique uses historical observations and model forecasts to calculate a weight for each ensemble member. We compare the weighted ensemble predictions with measured data to evaluate performance against present state-of-the-art. Finally, we discuss how this framework that integrates data-driven and physics-based approaches can outperform either technique in isolation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Marine Systems - Volume 186, October 2018, Pages 29-36
Journal: Journal of Marine Systems - Volume 186, October 2018, Pages 29-36
نویسندگان
Fearghal O'Donncha, Yushan Zhang, Bei Chen, Scott C. James,