04312nam 22007815 450 991048439440332120251113193939.03-030-64777-310.1007/978-3-030-64777-3(CKB)4900000000508898(MiAaPQ)EBC6465115(PPN)253253209(Au-PeEL)EBL6465115(OCoLC)1236264783(DE-He213)978-3-030-64777-3(EXLCZ)99490000000050889820210127d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning for Hydrometeorology and Environmental Science /by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (xiv, 204 pages) illustrations, mapsWater Science and Technology Library,1872-4663 ;993-030-64776-5 Introduction -- 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.This 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.Water Science and Technology Library,1872-4663 ;99WaterHydrologyArtificial intelligenceEnvironmental sciencesMathematicsEcologyNeural networks (Computer science)Human ecologyStudy and teachingWaterArtificial IntelligenceMathematical Applications in Environmental ScienceEnvironmental SciencesMathematical Models of Cognitive Processes and Neural NetworksEnvironmental StudiesWater.Hydrology.Artificial intelligence.Environmental sciencesMathematics.Ecology.Neural networks (Computer science)Human ecologyStudy and teaching.Water.Artificial Intelligence.Mathematical Applications in Environmental Science.Environmental Sciences.Mathematical Models of Cognitive Processes and Neural Networks.Environmental Studies.006.31Lee Taesam1081682Singh V. P(Vijay P.),Cho Kyung HwaMiAaPQMiAaPQMiAaPQBOOK9910484394403321Deep Learning for Hydrometeorology and Environmental Science4464116UNINA