Articles | Volume 17, issue 7
https://doi.org/10.5194/hess-17-2827-2013
© Author(s) 2013. 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-17-2827-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Jul 2013
Research article |
| 17 Jul 2013
Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework
N. J. Mount
School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
C. W. Dawson
Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK
R. J. Abrahart
School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
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Cited
14 citations as recorded by crossref.
- Use of exploratory fitness landscape metrics to better understand the impact of model structure on the difficulty of calibrating artificial neural network models S. Zhu et al. https://doi.org/10.1016/j.jhydrol.2022.128093
- Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades https://doi.org/10.5194/nhess-14-1641-2014
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen https://doi.org/10.3390/w10091116
- A review of artificial neural network models for ambient air pollution prediction S. Cabaneros et al. https://doi.org/10.1016/j.envsoft.2019.06.014
- Sensitivity Analysis of Empirical and Data-Driven Models on Longitudinal Dispersion Coefficient in Streams H. Nezaratian et al. https://doi.org/10.1007/s40710-018-0334-3
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support S. Razavi et al. https://doi.org/10.1016/j.envsoft.2020.104954
- Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. https://doi.org/10.1016/j.envsoft.2013.12.016
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683
- A comparison of artificial intelligence-based classification techniques in predicting flow variables in sharp curved channels A. Gholami et al. https://doi.org/10.1007/s00366-018-00697-7
- Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling D. Radford et al. https://doi.org/10.1016/j.jenvman.2024.123086
- Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models X. Li et al. https://doi.org/10.1016/j.envsoft.2014.11.028
- Predicting Stock Market Returns Using Sentiment in Business News Articles: An LSTM Machine Learning Approach J. Iqbal et al. https://doi.org/10.1177/21582440251415069
- Evolutionary Modeling of Response of Water Table to Precipitations A. Doglioni & V. Simeone https://doi.org/10.1061/(ASCE)HE.1943-5584.0001465
- 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
14 citations as recorded by crossref.
- Use of exploratory fitness landscape metrics to better understand the impact of model structure on the difficulty of calibrating artificial neural network models S. Zhu et al. https://doi.org/10.1016/j.jhydrol.2022.128093
- Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades https://doi.org/10.5194/nhess-14-1641-2014
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen https://doi.org/10.3390/w10091116
- A review of artificial neural network models for ambient air pollution prediction S. Cabaneros et al. https://doi.org/10.1016/j.envsoft.2019.06.014
- Sensitivity Analysis of Empirical and Data-Driven Models on Longitudinal Dispersion Coefficient in Streams H. Nezaratian et al. https://doi.org/10.1007/s40710-018-0334-3
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support S. Razavi et al. https://doi.org/10.1016/j.envsoft.2020.104954
- Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. https://doi.org/10.1016/j.envsoft.2013.12.016
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683
- A comparison of artificial intelligence-based classification techniques in predicting flow variables in sharp curved channels A. Gholami et al. https://doi.org/10.1007/s00366-018-00697-7
- Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling D. Radford et al. https://doi.org/10.1016/j.jenvman.2024.123086
- Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models X. Li et al. https://doi.org/10.1016/j.envsoft.2014.11.028
- Predicting Stock Market Returns Using Sentiment in Business News Articles: An LSTM Machine Learning Approach J. Iqbal et al. https://doi.org/10.1177/21582440251415069
- Evolutionary Modeling of Response of Water Table to Precipitations A. Doglioni & V. Simeone https://doi.org/10.1061/(ASCE)HE.1943-5584.0001465
- 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
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