Data-driven approaches for runoff prediction using distributed data
- 24 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Stochastic Environmental Research and Risk Assessment
- Vol. 36 (8), 2153-2171
- https://doi.org/10.1007/s00477-021-01993-3
Abstract
No abstract availableKeywords
This publication has 47 references indexed in Scilit:
- Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed dataJournal of Hydrology, 2012
- Data-driven modelling: some past experiences and new approachesJournal of Hydroinformatics, 2008
- Rainfall‐runoff modelling using artificial neural networks: comparison of network typesHydrological Processes, 2004
- Information Theory and Neural Networks for Managing Uncertainty in Flood RoutingJournal of Computing in Civil Engineering, 2004
- Managing uncertainty in hydrological models using complementary modelsHydrological Sciences Journal, 2003
- Model trees as an alternative to neural networks in rainfall—runoff modellingHydrological Sciences Journal, 2003
- Artificial Neural Networks in Remote Sensing of Hydrologic ProcessesJournal of Hydrologic Engineering, 2000
- Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applicationsEnvironmental Modelling & Software, 2000
- Long Short-Term MemoryNeural Computation, 1997
- A logical calculus of the ideas immanent in nervous activityBulletin of Mathematical Biology, 1943