- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks ; volume:22 ; number:11 ; year:2018 ; pages:6005-6022
Hydrology and earth system sciences ; 22, Heft 11 (2018), 6005-6022
- Creator
- DOI
-
10.5194/hess-22-6005-2018
- URN
-
urn:nbn:de:101:1-2018112903445975251971
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:03 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kratzert, Frederik
- Klotz, Daniel
- Brenner, Claire
- Schulz, Karsten
- Herrnegger, Mathew
Other Objects (12)
Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks
The PDM rainfall-runoff model
Rainfall-runoff modeling in arid areas
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
Towards a comprehensive physically-based rainfall-runoff model
CLASSIC: a semi-distributed rainfall-runoff modelling system
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks
The PDM rainfall-runoff model
Rainfall-runoff modeling in arid areas
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
Towards a comprehensive physically-based rainfall-runoff model
CLASSIC: a semi-distributed rainfall-runoff modelling system
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks
The PDM rainfall-runoff model
Rainfall-runoff modeling in arid areas
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
Towards a comprehensive physically-based rainfall-runoff model
CLASSIC: a semi-distributed rainfall-runoff modelling system
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network