Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering
The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured mes...
Gardado en:
| Formato: | Online |
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| Idioma: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Subjects: | |
| Acceso en liña: | ONIX_20220111_9783036509563_233 |
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| _version_ | 1869515706701185024 |
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| collection | Directory of Open Access Books |
| description | The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers |
| format | Online |
| id | doab-20.500.12854ir-76497 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-764972024-03-27T16:34:23Z Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering Fang, Fangxin numerical modelling unstructured meshes finite volume North Sea salinity deep learning martinez boundary salinity generator Sacramento–San Joaquin Delta residence time exposure time transport time scale hyper-tidal estuary singular value decomposition data assimilation ocean models observation strategies ocean forecasting systems ocean Double Gyre 4D-Var ROMS MEOF initial ensemble ensemble spread LETKF n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers 2022-01-11T13:33:34Z 2022-01-11T13:33:34Z 2021 book ONIX_20220111_9783036509563_233 9783036509563 9783036509570 https://directory.doabooks.org/handle/20.500.12854/76497 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/3943 https://mdpi.com/books/pdfview/book/3943 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-0957-0 10.3390/books978-3-0365-0957-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036509563 9783036509570 110 Basel, Switzerland open access |
| spellingShingle | numerical modelling unstructured meshes finite volume North Sea salinity deep learning martinez boundary salinity generator Sacramento–San Joaquin Delta residence time exposure time transport time scale hyper-tidal estuary singular value decomposition data assimilation ocean models observation strategies ocean forecasting systems ocean Double Gyre 4D-Var ROMS MEOF initial ensemble ensemble spread LETKF n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title | Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title_full | Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title_fullStr | Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title_full_unstemmed | Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title_short | Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering |
| title_sort | numerical and data driven modelling in coastal hydrological and hydraulic engineering |
| topic | numerical modelling unstructured meshes finite volume North Sea salinity deep learning martinez boundary salinity generator Sacramento–San Joaquin Delta residence time exposure time transport time scale hyper-tidal estuary singular value decomposition data assimilation ocean models observation strategies ocean forecasting systems ocean Double Gyre 4D-Var ROMS MEOF initial ensemble ensemble spread LETKF n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | numerical modelling unstructured meshes finite volume North Sea salinity deep learning martinez boundary salinity generator Sacramento–San Joaquin Delta residence time exposure time transport time scale hyper-tidal estuary singular value decomposition data assimilation ocean models observation strategies ocean forecasting systems ocean Double Gyre 4D-Var ROMS MEOF initial ensemble ensemble spread LETKF n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20220111_9783036509563_233 |