TY  - GEN
N2  - 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).
DO  - 10.1007/978-3-030-64777-3
DO  - doi
AB  - 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).
T1  - Deep learning for hydrometerology and environmental science /
AU  - Lee, Taesam,
AU  - Singh, V. P.
AU  - Cho, Kyung Hwa,
VL  - volume 99
CN  - GB2801.72.E45
ID  - 1433593
KW  - Hydrometeorology
KW  - Machine learning.
KW  - Depth-area-duration (Hydrometeorology)
KW  - Hydrométéorologie
KW  - Apprentissage automatique.
KW  - Hauteur-superficie-durée (Hydrométéorologie)
SN  - 3030647773
SN  - 9783030647773
TI  - Deep learning for hydrometerology and environmental science /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-64777-3
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-64777-3
ER  -