Articles | Volume 16, issue 8
https://doi.org/10.5194/hess-16-3049-2012
© Author(s) 2012. 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-16-3049-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Aug 2012
Research article |
| 29 Aug 2012
Ideal point error for model assessment in data-driven river flow forecasting
C. W. Dawson
Department of Computer Science, Loughborough University, Loughborough, UK
N. J. Mount
School of Geography, University of Nottingham, Nottingham, UK
R. J. Abrahart
School of Geography, University of Nottingham, Nottingham, UK
A. Y. Shamseldin
Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand
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- A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. https://doi.org/10.1016/j.envsoft.2019.01.005
- Linking plant and soil indices for water stress management in black gram A. Khorsand et al. https://doi.org/10.1038/s41598-020-79516-3
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu https://doi.org/10.3390/w11040823
- Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites D. Salazar-Martínez et al. https://doi.org/10.1016/j.jhydrol.2022.127786
- Projected changes of runoff in the Upper Yellow River Basin under shared socioeconomic pathways Z. Chen et al. https://doi.org/10.1007/s11707-022-1032-z
- Improved validation framework and R-package for artificial neural network models G. Humphrey et al. https://doi.org/10.1016/j.envsoft.2017.01.023
- Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework N. Mount et al. https://doi.org/10.5194/hess-17-2827-2013
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15 citations as recorded by crossref.
- Review and comparison of performance indices for automatic model induction J. Chadalawada & V. Babovic https://doi.org/10.2166/hydro.2017.078
- Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization J. Abbot & J. Marohasy https://doi.org/10.1016/j.atmosres.2017.07.015
- Multi-time-step ahead daily global solar radiation forecasting: performance evaluation of wavelet-based artificial neural network model S. Sharifi et al. https://doi.org/10.1007/s00703-022-00882-w
- A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. https://doi.org/10.1016/j.envsoft.2019.01.005
- Linking plant and soil indices for water stress management in black gram A. Khorsand et al. https://doi.org/10.1038/s41598-020-79516-3
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu https://doi.org/10.3390/w11040823
- Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites D. Salazar-Martínez et al. https://doi.org/10.1016/j.jhydrol.2022.127786
- Projected changes of runoff in the Upper Yellow River Basin under shared socioeconomic pathways Z. Chen et al. https://doi.org/10.1007/s11707-022-1032-z
- Improved validation framework and R-package for artificial neural network models G. Humphrey et al. https://doi.org/10.1016/j.envsoft.2017.01.023
- Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework N. Mount et al. https://doi.org/10.5194/hess-17-2827-2013
- An improved risk-benefit collaborative grey target decision model and its application in the decision making of load adjustment schemes R. Li et al. https://doi.org/10.1016/j.energy.2018.05.119
- Investigating most appropriate method for estimating suspended sediment load based on error criterias in arid and semi-arid areas (case study of Kardeh Dam watershed stations) H. Mousazadeh et al. https://doi.org/10.1007/s12517-021-08414-3
- Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts J. Zaherpour et al. https://doi.org/10.1088/1748-9326/aac547
- Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models J. Zaherpour et al. https://doi.org/10.1016/j.envsoft.2019.01.003
- OpenBench: a land model evaluation system Z. Wei et al. https://doi.org/10.5194/gmd-18-6517-2025
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