Articles | Volume 14, issue 3
https://doi.org/10.5194/hess-14-603-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-14-603-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
30 Mar 2010
An experiment on the evolution of an ensemble of neural networks for streamflow forecasting
M.-A. Boucher
Chaire de recherche EDS en prévisions et actions hydrologiques, Département de génie civil, Université Laval, Pavillon Pouliot, Québec, G1K 7P4, Canada
J.-P. Laliberté
Chaire de recherche EDS en prévisions et actions hydrologiques, Département de génie civil, Université Laval, Pavillon Pouliot, Québec, G1K 7P4, Canada
F. Anctil
Chaire de recherche EDS en prévisions et actions hydrologiques, Département de génie civil, Université Laval, Pavillon Pouliot, Québec, G1K 7P4, Canada
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- Potential application of wavelet neural network ensemble to forecast streamflow for flood management K. Kasiviswanathan et al. 10.1016/j.jhydrol.2016.02.044
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- Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms H. Erdal & O. Karakurt 10.1016/j.jhydrol.2012.11.015
- Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations K. Kasiviswanathan et al. 10.1016/j.jhydrol.2013.06.043
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- Using artificial neural networks to estimate snow water equivalent from snow depth J. Odry et al. 10.1080/07011784.2020.1796817
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
- Ensemble machine learning paradigms in hydrology: A review M. Zounemat-Kermani et al. 10.1016/j.jhydrol.2021.126266
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- Process Capability Analysis via Continuous Ranked Probability Score L. Shi et al. 10.1002/qre.1967
- Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns – A Case Study with Potato J. Fortin et al. 10.1080/07038992.2014.928182
- Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
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30 citations as recorded by crossref.
- Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river A. Piotrowski et al. 10.1016/j.cageo.2013.12.013
- A dynamic artificial neural network for tomato yield prediction R. Salazar et al. 10.17660/ActaHortic.2017.1154.11
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
- Potential application of wavelet neural network ensemble to forecast streamflow for flood management K. Kasiviswanathan et al. 10.1016/j.jhydrol.2016.02.044
- Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study F. Granata et al. 10.1016/j.jhydrol.2022.128431
- Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin P. Parasar & A. Krishna 10.1038/s41598-025-13221-x
- Implications of uncertainty in inflow forecasting on reservoir operation for irrigation K. Kasiviswanathan et al. 10.1007/s10333-020-00822-7
- Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms H. Erdal & O. Karakurt 10.1016/j.jhydrol.2012.11.015
- Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations K. Kasiviswanathan et al. 10.1016/j.jhydrol.2013.06.043
- Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria D. Brochero et al. 10.5194/hess-15-3307-2011
- Using artificial neural networks to estimate snow water equivalent from snow depth J. Odry et al. 10.1080/07011784.2020.1796817
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
- Ensemble machine learning paradigms in hydrology: A review M. Zounemat-Kermani et al. 10.1016/j.jhydrol.2021.126266
- Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. 10.5194/hess-16-1151-2012
- Quantification of the predictive uncertainty of artificial neural network based river flow forecast models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-012-0600-2
- On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. 10.1080/02626667.2015.1085650
- Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models Y. Chiang et al. 10.1080/02626667.2017.1330543
- Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts? M. De Santi et al. 10.1371/journal.pwat.0000040
- Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons M. Boucher et al. 10.1029/2019WR026226
- Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
- Quantification of parametric uncertainty of ANN models with GLUE method for different streamflow dynamics H. Tongal & M. Booij 10.1007/s00477-017-1408-x
- Process Capability Analysis via Continuous Ranked Probability Score L. Shi et al. 10.1002/qre.1967
- Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns – A Case Study with Potato J. Fortin et al. 10.1080/07038992.2014.928182
- Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
- Comparison of physically based and empirical models to estimate corn (Zea maysL) LAI from multispectral data in eastern Canada J. Fortin et al. 10.5589/m13-010
- Investigating ANN architectures and training to estimate snow water equivalent from snow depth K. Ntokas et al. 10.5194/hess-25-3017-2021
- Euphausiid respiration model revamped: Latitudinal and seasonal shaping effects on krill respiration rates N. Tremblay et al. 10.1016/j.ecolmodel.2014.07.031
- Training for the test: Using multi-objective training to improve ANN ensemble forecasts of household residual chlorine in emergencies M. De Santi et al. 10.1371/journal.pwat.0000307
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