All Outputs (3)

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.