Outputs (20)

Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan (2016)
Journal Article
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F.-J., & Abrahart, R. J. (2016). Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan. Hydrological Sciences Journal, 61(7), 1192-1208. https://doi.org/10.1080/02626667.2016.1159683

© 2016 IAHS. “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 occurrin... Read More about Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan.

Delivering and evaluating the multiple flood risk benefits in Blue-Green Cities: an interdisciplinary approach (2014)
Presentation / Conference Contribution
Lawson, E., Thorne, C., Ahilan, S., Allen, D., Arthur, S., Everett, G., Fenner, R., Glenis, V., Guan, D., Hoang, L., Kilsby, C., Lamond, J., Mant, J., Maskrey, S., Mount, N., Sleigh, A., Smith, L., & Wright, N. (2014, June). Delivering and evaluating the multiple flood risk benefits in Blue-Green Cities: an interdisciplinary approach. Presented at FRIAR 2014, Poznan, Poland

A Blue-Green City aims to recreate a naturally-oriented water cycle while contributing to the amenity of the city by bringing water management and green infrastructure together. The Blue-Green approach is more than a stormwater management strategy ai... Read More about Delivering and evaluating the multiple flood risk benefits in Blue-Green Cities: an interdisciplinary approach.

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.-J. (2014). Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan. Hydrological Processes, 28(3), https://doi.org/10.1002/hyp.9559

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 about Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan.

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), https://doi.org/10.2166/hydro.2013.222

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 about Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models.

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. https://doi.org/10.5194/hess-17-2827-2013

In this paper the difficult problem of how to legitimise data-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-... Read More about Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework.

Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh (2013)
Journal Article
Mount, N. J., Tate, N. J., Sarker, M. H., & Thorne, C. R. (2013). Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh. Geomorphology, 183, https://doi.org/10.1016/j.geomorph.2012.07.017

In this study continuous wavelet transforms are used to explore spatio-temporal patterns of multi-scale bank line retreat along a 204 km reach of the Jamuna River, Bangladesh. A sequence of eight bank line retreat series, derived from remotely-sense... Read More about Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh.

The need for operational reasoning in data-driven rating curve prediction of suspended sediment (2012)
Journal Article
Mount, N. J., Abrahart, R., Dawson, C., & Ab Ghani, N. (2012). The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrological Processes, 26(26), https://doi.org/10.1002/hyp.8439

The use of data-driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques a... Read More about The need for operational reasoning in data-driven rating curve prediction of suspended sediment.

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), https://doi.org/10.1080/02626667.2012.658401

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 about Neuroemulation: definition and key benefits for water resources research.

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), https://doi.org/10.5194/hess-16-3049-2012

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 about Ideal point error for model assessment in data-driven river flow forecasting.

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), https://doi.org/10.1016/j.jhydrol.2011.08.054

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 about DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling.