LEADER 04312nam 22007815 450 001 9910484394403321 005 20251113193939.0 010 $a3-030-64777-3 024 7 $a10.1007/978-3-030-64777-3 035 $a(CKB)4900000000508898 035 $a(MiAaPQ)EBC6465115 035 $a(PPN)253253209 035 $a(Au-PeEL)EBL6465115 035 $a(OCoLC)1236264783 035 $a(DE-He213)978-3-030-64777-3 035 $a(EXLCZ)994900000000508898 100 $a20210127d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for Hydrometeorology and Environmental Science /$fby Taesam Lee, Vijay P. Singh, Kyung Hwa Cho 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (xiv, 204 pages) $cillustrations, maps 225 1 $aWater Science and Technology Library,$x1872-4663 ;$v99 311 08$a3-030-64776-5 327 $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. 330 $aThis book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model. 410 0$aWater Science and Technology Library,$x1872-4663 ;$v99 606 $aWater 606 $aHydrology 606 $aArtificial intelligence 606 $aEnvironmental sciences$xMathematics 606 $aEcology 606 $aNeural networks (Computer science) 606 $aHuman ecology$xStudy and teaching 606 $aWater 606 $aArtificial Intelligence 606 $aMathematical Applications in Environmental Science 606 $aEnvironmental Sciences 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aEnvironmental Studies 615 0$aWater. 615 0$aHydrology. 615 0$aArtificial intelligence. 615 0$aEnvironmental sciences$xMathematics. 615 0$aEcology. 615 0$aNeural networks (Computer science) 615 0$aHuman ecology$xStudy and teaching. 615 14$aWater. 615 24$aArtificial Intelligence. 615 24$aMathematical Applications in Environmental Science. 615 24$aEnvironmental Sciences. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aEnvironmental Studies. 676 $a006.31 700 $aLee$b Taesam$01081682 702 $aSingh$b V. P$g(Vijay P.), 702 $aCho$b Kyung Hwa 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484394403321 996 $aDeep Learning for Hydrometeorology and Environmental Science$94464116 997 $aUNINA