LEADER 03389nam 2200637z- 450 001 9910557351203321 005 20220111 035 $a(CKB)5400000000042378 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76497 035 $a(oapen)doab76497 035 $a(EXLCZ)995400000000042378 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNumerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (110 p.) 311 08$a3-0365-0956-9 311 08$a3-0365-0957-7 330 $aThe 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 606 $aResearch & information: general$2bicssc 610 $a4D-Var 610 $adata assimilation 610 $adeep learning 610 $aensemble spread 610 $aexposure time 610 $afinite volume 610 $ahyper-tidal estuary 610 $ainitial ensemble 610 $aLETKF 610 $amartinez boundary salinity generator 610 $aMEOF 610 $an/a 610 $aNorth Sea 610 $anumerical modelling 610 $aobservation strategies 610 $aocean Double Gyre 610 $aocean forecasting systems 610 $aocean models 610 $aresidence time 610 $aROMS 610 $aSacramento-San Joaquin Delta 610 $asalinity 610 $asingular value decomposition 610 $atransport time scale 610 $aunstructured meshes 615 7$aResearch & information: general 700 $aFang$b Fangxin$4edt$01291787 702 $aFang$b Fangxin$4oth 906 $aBOOK 912 $a9910557351203321 996 $aNumerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering$93021921 997 $aUNINA