Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
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- 作者：Dominic E. Reevea ; d.e.reeve@swansea.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Harshinie Karunarathnaa ; Shunqi Panb ; Jose M. Horrillo-Caraballoa ; Grzegorz Ró ; żyńskic ; Roshanka Ranasinghed
- 关键词：Data-driven model ; Coastal morphological change ; Statistical analysis ; Hybrid model
- 刊名：Geomorphology
- 出版年：2016
- 出版时间：1 March 2016
- 年：2016
- 卷：256
- 期：Complete
- 页码：49-67
- 全文大小：2895 K
文摘
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This paper is about morphological prediction methods for meso-scale forecasting
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A range of data-driven and hybrid methods are illustrated.
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Increased availability of measurements provides the impetus for developing such methods.
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The nature of and insights provided by these methods is demonstrated through applications to several study sites.
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Data-driven methods can have an important role to play in coastal management decisions.