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Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review
Runoff prediction in ungauged and scarcely gauged catchments is a key research field in
surface water hydrology. There have been numerous studies before and since the launch of …
surface water hydrology. There have been numerous studies before and since the launch of …
[HTML][HTML] Sources of hydrological model uncertainties and advances in their analysis
Water | Free Full-Text | Review: Sources of Hydrological Model Uncertainties and Advances
in Their Analysis Next Article in Journal Assessing the Influence of Compounding Factors to …
in Their Analysis Next Article in Journal Assessing the Influence of Compounding Factors to …
[HTML][HTML] Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
Toward improved predictions in ungauged basins: Exploiting the power of machine learning
Long short‐term memory (LSTM) networks offer unprecedented accuracy for prediction in
ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS …
ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS …
Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning
Modeling dynamic geophysical phenomena is at the core of Earth and environmental
studies. The geoscientific community relying mainly on physical representations may want to …
studies. The geoscientific community relying mainly on physical representations may want to …
A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method
The important foundation for water resource management and utilization is effective monthly
runoff prediction. In this study, a new coupled model for predicting monthly runoff is …
runoff prediction. In this study, a new coupled model for predicting monthly runoff is …
Global‐scale regionalization of hydrologic model parameters
Current state‐of‐the‐art models typically applied at continental to global scales (hereafter
called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …
called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …
Global evaluation of runoff from 10 state-of-the-art hydrological models
Observed streamflow data from 966 medium sized catchments (1000–5000 km 2) around
the globe were used to comprehensively evaluate the daily runoff estimates (1979–2012) of …
the globe were used to comprehensively evaluate the daily runoff estimates (1979–2012) of …
Alternate pathway for regional flood frequency analysis in data-sparse region
Accurately analyzing flood frequency is crucial for develo** effective flood management
strategies and designing flood protection infrastructure, but the complex and nonlinear …
strategies and designing flood protection infrastructure, but the complex and nonlinear …