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Improved validation framework and R-package for artificial neural network models (2017)
Journal Article
Humphrey, G. B., Maier, H. R., Wu, W., Mount, N. J., Dandy, G. C., Abrahart, R., & Dawson, C. (2017). Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, doi:10.1016/j.envsoft.2017.01.023. ISSN 1364-8152

Validation is a critical component of any modelling process. In artificial neural network (ANN) modelling, validation generally consists of the assessment of model predictive performance on an independent validation set (predictive validity). However... Read More

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan (2014)
Journal Article
Meng-Jung, T., Abrahart, R., Mount, N. J., & Chang, F. (2014). Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan. Hydrological Processes, 28(3), doi:10.1002/hyp.9559. ISSN 0885-6087

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. Howeve... Read More

Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models (2014)
Journal Article
Dawson, C., Mount, N. J., Abrahart, R., & Louis, J. (2014). Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models. Journal of Hydroinformatics, 16(2), doi:10.2166/hydro.2013.222. ISSN 1464-7141

This paper addresses the difficult question of how to perform meaningful comparisons between neural network-based hydrological models and alternative modelling approaches. Standard, goodness-of-fit metric approaches are limited since they only assess... Read More

Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework (2013)
Journal Article
Mount, N. J., Dawson, C., & Abrahart, R. (2013). Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework. Hydrology and Earth System Sciences, 17(7), 2827-2843. doi:10.5194/hess-17-2827-2013

In this paper the difficult problem of how to legitimisedata-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-b... Read More

Neuroemulation: definition and key benefits for water resources research (2012)
Journal Article
Abrahart, R., Mount, N. J., & Shamseldin, A. (2012). Neuroemulation: definition and key benefits for water resources research. Hydrological Sciences Journal, 57(3), doi:10.1080/02626667.2012.658401. ISSN 0262-6667

Neuroemulation is the art and science of using a neural network model to replicate the external behaviour of some other model and it is an activity that is distinct from neural-network-based simulation. Whilst is has become a recognised and establish... Read More

Ideal point error for model assessment in data-driven river flow forecasting (2012)
Journal Article
Dawson, C., Mount, N. J., Abrahart, R., & Shamseldin, A. (2012). Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16(8), doi:10.5194/hess-16-3049-2012. ISSN 1027-5606

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consisten... Read More

DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling (2011)
Journal Article
Abrahart, R., Mount, N. J., Ab Ghani, N., Clifford, N. J., & Dawson, C. (2011). DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling. Journal of Hydrology, 409(3-4), doi:10.1016/j.jhydrol.2011.08.054. ISSN 0022-1694

The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding... Read More

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan
Journal Article
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F., & Abrahart, R. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan. Hydrological Sciences Journal, ISSN 0262-6667

“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the inte... Read More