001433593 000__ 03042cam\a2200613\i\4500
001433593 001__ 1433593
001433593 003__ OCoLC
001433593 005__ 20230309003613.0
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001433593 007__ cr\un\nnnunnun
001433593 008__ 210129s2021\\\\sz\\\\\\ob\\\\000\0\eng\d
001433593 019__ $$a1236264783$$a1241065892
001433593 020__ $$a3030647773$$q(electronic book)
001433593 020__ $$a9783030647773$$q(electronic bk.)
001433593 020__ $$z3030647765
001433593 020__ $$z9783030647766
001433593 0247_ $$a10.1007/978-3-030-64777-3$$2doi
001433593 035__ $$aSP(OCoLC)1235282057
001433593 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dUCW$$dYDXIT$$dOCLCO$$dN$T$$dOCLCF$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCQ$$dDKU$$dUKAHL$$dOCLCQ$$dOCLCO$$dOCLCQ
001433593 049__ $$aISEA
001433593 050_4 $$aGB2801.72.E45$$bL44 2021
001433593 08204 $$a551.570285$$223
001433593 1001_ $$aLee, Taesam,$$eauthor.
001433593 24510 $$aDeep learning for hydrometerology and environmental science /$$cTaesam Lee, Vijay P. Singh, Kyung Hwa Cho.
001433593 264_1 $$aCham, Switzerland :$$bSpringer Nature,$$c[2021]
001433593 300__ $$a1 online resource
001433593 336__ $$atext$$btxt$$2rdacontent
001433593 337__ $$acomputer$$bc$$2rdamedia
001433593 338__ $$aonline resource$$bcr$$2rdacarrier
001433593 347__ $$atext file
001433593 347__ $$bPDF
001433593 4901_ $$aWater science and technology library ;$$vvolume 99
001433593 504__ $$aIncludes bibliographical references.
001433593 5050_ $$aIntroduction -- Mathematical Background -- Data Preprocessing -- Neural Network -- Training a Neural Network -- Updating Weights -- Improving model performance -- Advanced Neural Network Algorithms -- Deep learning for time series -- Deep learning for spatial datasets -- Tensorflow and Keras Programming for Deep Learning -- Hydrometeorological Applications of deep learning -- Environmental Applications of deep learning.
001433593 506__ $$aAccess limited to authorized users.
001433593 520__ $$aThis book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality).
001433593 588__ $$aOnline resource; title from digital title page (viewed on February 11, 2021).
001433593 650_0 $$aHydrometeorology$$xData processing.
001433593 650_0 $$aMachine learning.
001433593 650_0 $$aDepth-area-duration (Hydrometeorology)
001433593 650_6 $$aHydrométéorologie$$xInformatique.
001433593 650_6 $$aApprentissage automatique.
001433593 650_6 $$aHauteur-superficie-durée (Hydrométéorologie)
001433593 655_0 $$aElectronic books.
001433593 7001_ $$aSingh, V. P.$$q(Vijay P.),$$eauthor.
001433593 7001_ $$aCho, Kyung Hwa,$$eauthor.
001433593 77608 $$iPrint version:$$z3030647765$$z9783030647766$$w(OCoLC)1202747744
001433593 830_0 $$aWater science and technology library ;$$vv. 99.
001433593 852__ $$bebk
001433593 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-64777-3$$zOnline Access$$91397441.1
001433593 909CO $$ooai:library.usi.edu:1433593$$pGLOBAL_SET
001433593 980__ $$aBIB
001433593 980__ $$aEBOOK
001433593 982__ $$aEbook
001433593 983__ $$aOnline
001433593 994__ $$a92$$bISE