Robert J. Abrahart

Environmental Science · Physical Sciences

27h-index3.6kcitations98works0.02yr avg
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Research Topics

Hydrological Forecasting Using AI(78), Hydrology and Watershed Management Studies(69), Flood Risk Assessment and Management(27), Neural Networks and Applications(16), Hydrology and Drought Analysis(12)

Publications98 total

How useful are “validated” artificial neural network models for increasing environmental system understanding?
ScholarsArchive (Brigham Young University)·2018Open Access
Improved validation framework and R-package for artificial neural network models
Environmental Modelling & Software·2017· 65 citedOpen Access
On the Physical and Operational Rationality of Data-Driven Models for Suspended Sediment Prediction in Rivers
2016Open Access
Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan
Hydrological Sciences Journal·2016· 103 citedOpen Access
Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework
Hydrology and earth system sciences·2013· 18 citedOpen Access
Sensitivity analysis for comparison, validation and physical legitimacy of neural network-based hydrological models
Journal of Hydroinformatics·2013· 15 citedOpen Access
A typology of different development and testing options for symbolic regression modelling of measured and calculated datasets
Environmental Modelling & Software·2013· 3 citedOpen Access
Legitimising neural network river forecasting models: a new data-driven mechanistic modelling framework
2013· 1 citedOpen Access
Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan
Hydrological Processes·2012· 41 citedOpen Access
Ideal point error for model assessment in data-driven river flow forecasting
Hydrology and earth system sciences·2012· 17 citedOpen Access
River flow forecasting using gene expression programming models
Unitec Research Bank (Unitec Institute of Technology)·2012· 6 citedOpen Access
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Progress in Physical Geography Earth and Environment·2012· 309 citedOpen Access
Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]
Water Resources Management·2012· 3 cited
“Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations” by Jalal Shiri & Ozgur Kisi [Computers and Geosciences (2011) 1692–1701]
Computers & Geosciences·2012· 2 cited
Modelling a model?!! Prediction of observed and calculated daily pan evaporation in New Mexico, U.S.A.
EGUGA·2012
How much complexity is warranted in a rainfall-runoff model? Findings obtained from symbolic regression, using Eureqa
EGUGA·2012· 5 cited
Neuroemulation: definition and key benefits for water resources research
Hydrological Sciences Journal·2012· 11 citedOpen Access
Ideal point error for model assessment
2012Open Access
Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models
Journal of Hydrologic Engineering·2012· 35 cited
Comparison of two data-driven approaches for daily river flow forecasting
Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.·2011· 6 citedOpen Access
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Frequent Co-authors

Linda See(24), Christian W. Dawson(18), Nick J. Mount(13), Asaad Y. Shamseldin(11), Dimitri Solomatine(5), Robert L. Wilby(5), Pauline E. Kneale(4), Elena Toth(3), Holger R. Maier(3), Alison Heppenstall(3), Ngahzaifa Ab Ghani(3), François Anctil(2), Fi‐John Chang(2), M. J. Kirkby(2), J. B. Thornes(2), Graeme C. Dandy(2), Achela Fernando(2), Paulin Coulibaly(1), D. P. Solomatine(1), Amin Elshorbagy(1)