Articles | Volume 10, issue 4
https://doi.org/10.5194/hess-10-485-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/hess-10-485-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
07 Jul 2006
Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts
N. Lauzon
Golder Associates, Burnaby, BC, Canada
F. Anctil
Département de génie civil, Pavillon Pouliot, Université Laval, Québec, G1K 7P4, Canada
C. W. Baxter
HYDRANNT Consulting Inc., Port Coquitlam, Canada
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Cited
15 citations as recorded by crossref.
- Using multi-temporal analysis to classify monthly precipitation based on maximal overlap discrete wavelet transform K. Roushangar & F. Alizadeh https://doi.org/10.2166/hydro.2019.021
- Comparison of classification and clustering methods in spatial rainfall pattern recognition at Northern Iran S. Golian et al. https://doi.org/10.1007/s00704-010-0267-x
- Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment G. Lischeid https://doi.org/10.1007/s00477-008-0266-y
- Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps K. Roushangar et al. https://doi.org/10.2166/wcc.2019.097
- Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E. Snieder et al. https://doi.org/10.5194/hess-25-2543-2021
- Baseflow separation techniques for modular artificial neural network modelling in flow forecasting G. CORZO & D. SOLOMATINE https://doi.org/10.1623/hysj.52.3.491
- Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions H. Maier et al. https://doi.org/10.1016/j.envsoft.2010.02.003
- Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network K. Hsu & S. Li https://doi.org/10.1016/j.advwatres.2009.11.005
- On the criteria of model performance evaluation for real-time flood forecasting K. Cheng et al. https://doi.org/10.1007/s00477-016-1322-7
- Current awareness https://doi.org/10.1002/hyp.6661
- A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique K. Roushangar & F. Alizadeh https://doi.org/10.1007/s11629-017-4684-5
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. https://doi.org/10.5194/hess-13-1607-2009
- Artificial Intelligence Techniques as Detection Tests for the Identification of Shifts in Hydrometric Data N. Lauzon & B. Lence https://doi.org/10.1061/(ASCE)CP.1943-5487.0000042
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. https://doi.org/10.1002/hyp.9559
- Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering N. de Vos et al. https://doi.org/10.1002/hyp.7698
15 citations as recorded by crossref.
- Using multi-temporal analysis to classify monthly precipitation based on maximal overlap discrete wavelet transform K. Roushangar & F. Alizadeh https://doi.org/10.2166/hydro.2019.021
- Comparison of classification and clustering methods in spatial rainfall pattern recognition at Northern Iran S. Golian et al. https://doi.org/10.1007/s00704-010-0267-x
- Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment G. Lischeid https://doi.org/10.1007/s00477-008-0266-y
- Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps K. Roushangar et al. https://doi.org/10.2166/wcc.2019.097
- Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E. Snieder et al. https://doi.org/10.5194/hess-25-2543-2021
- Baseflow separation techniques for modular artificial neural network modelling in flow forecasting G. CORZO & D. SOLOMATINE https://doi.org/10.1623/hysj.52.3.491
- Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions H. Maier et al. https://doi.org/10.1016/j.envsoft.2010.02.003
- Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network K. Hsu & S. Li https://doi.org/10.1016/j.advwatres.2009.11.005
- On the criteria of model performance evaluation for real-time flood forecasting K. Cheng et al. https://doi.org/10.1007/s00477-016-1322-7
- Current awareness https://doi.org/10.1002/hyp.6661
- A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique K. Roushangar & F. Alizadeh https://doi.org/10.1007/s11629-017-4684-5
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. https://doi.org/10.5194/hess-13-1607-2009
- Artificial Intelligence Techniques as Detection Tests for the Identification of Shifts in Hydrometric Data N. Lauzon & B. Lence https://doi.org/10.1061/(ASCE)CP.1943-5487.0000042
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. https://doi.org/10.1002/hyp.9559
- Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering N. de Vos et al. https://doi.org/10.1002/hyp.7698
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