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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

Research & information: general --- 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 --- Sacramento-San Joaquin Delta

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

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 --- Sacramento-San Joaquin Delta