Robert Abrahart

Contact

• workRoom C21 Sir Clive Granger Building
  University Park
  Nottingham
  NG7 2RD
  UK
• work0115 84 66145
• fax0115 951 5249
• bob.abrahart@nottingham.ac.uk

Research Summary

Research interests are focused on the identification and appropriate utilisation of modern computation-based technologies for geographical analysis, spatial modelling and in-silica exploration.… read more

Recent Publications

• BERIRO, DJ, ABRAHART, RJ and NATHANAIL, CP, 2012. Comments On "Empirical Modelling Of Plate Load Test Moduli Of Soil Via Gene Expression Programming" By Ali Mollahasani, Amir Hossein Alavi And Amir Hossein Gandomi [Computers And Geotechnics 38 (2011) 281-286]
• MOUNT, NJ and ABRAHART, RJ, 2011. Discussion Of "River Flow Estimation From Upstream Flow Records By Artificial Intelligence Methods" By Me Turan, Ma Yurdusev [J. Hydrol. 369 (2009) 71-77]
• DOMINGUEZ, E, DAWSON, CW, RAMIREZ, A and ABRAHART, RJ, 2011. The Search For Orthogonal Hydrological Modelling Metrics: A Case Study Of 20 Monitoring Stations In Colombia Journal Of Hydroinformatics. 13(3), 429-442
• MOUNT, NJ and ABRAHART, RJ, 2011. Load Or Concentration, Logged Or Unlogged? Addressing Ten Years Of Uncertainty In Neural Network Suspended Sediment Prediction Hydrological Processes. 25(20), 3144-3157

• View all publications

Current Research

Research interests are focused on the identification and appropriate utilisation of modern computation-based technologies for geographical analysis, spatial modelling and in-silica exploration. Neurocomputing has to date been the main topic of investigation, in particular exploring the potential benefits and pitfalls that are associated with the use of artificial neural networks, either alone, or in association with other computational devices such as genetic algorithms and self-organising maps. These emergent data-driven technologies have been used to perform different types of geographical and hydrological modelling and to develop alternative solutions.

• BERIRO, DJ, ABRAHART, RJ and NATHANAIL, CP, 2012. Comments On "Empirical Modelling Of Plate Load Test Moduli Of Soil Via Gene Expression Programming" By Ali Mollahasani, Amir Hossein Alavi And Amir Hossein Gandomi [Computers And Geotechnics 38 (2011) 281-286]
• MOUNT, NJ and ABRAHART, RJ, 2011. Discussion Of "River Flow Estimation From Upstream Flow Records By Artificial Intelligence Methods" By Me Turan, Ma Yurdusev [J. Hydrol. 369 (2009) 71-77]
• DOMINGUEZ, E, DAWSON, CW, RAMIREZ, A and ABRAHART, RJ, 2011. The Search For Orthogonal Hydrological Modelling Metrics: A Case Study Of 20 Monitoring Stations In Colombia Journal Of Hydroinformatics. 13(3), 429-442
• MOUNT, NJ and ABRAHART, RJ, 2011. Load Or Concentration, Logged Or Unlogged? Addressing Ten Years Of Uncertainty In Neural Network Suspended Sediment Prediction Hydrological Processes. 25(20), 3144-3157
• ABRAHART, RJ, MOUNT, NJ, AB GHANI, N, CLIFFORD, NJ and DAWSON, CW, 2011. Damp: A Protocol For Contextualising Goodness-Of-Fit Statistics In Sediment-Discharge Data-Driven Modelling Journal Of Hydrology. 409(3-4), 596-611
• ABRAHART, RJ and MOUNT, NJ, 2011. Discussion Of "Neuro-Fuzzy Models Employing Wavelet Analysis For Suspended Sediment Concentration Prediction In Rivers"
• ABRAHART, RJ and AB GHANI, N, 2010. Neuro-Fuzzy Modelling Of Suspended Sediment Load: The Need For A Sound Comparison With Established Methods Advances In Engineering Software. 41(6), 874-878
• DAWSON, CW, ABRAHART, RJ and SEE, LM, 2010. Hydro Test: Further Development Of A Web Resource For The Standardised Assessment Of Hydrological Models Environmental Modelling & Software. 25(11), 1481-1482
• ABRAHART, RJ, DAWSON, CW, SEE, LM, MOUNT, NJ and SHAMSELDIN, AY, 2010. Discussion Of "Evapotranspiration Modelling Using Support Vector Machines"
• ABRAHART, RJ, AB GHANI, N and SWAN, J, 2009. Discussion Of "An Explicit Neural Network Formulation For Evapotranspiration"
• ABRAHART, R.J., HEPPENSTALL, A.J. and SEE, L.M., 2007. Timing error correction procedure applied to neural network rainfall-runoff modelling Hydrological Sciences Journal. 52(3), 414-431
• DAWSON, C.W., ABRAHART, R.J. and SEE, L.M., 2007. HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts Environmental Modelling & Software. 22(7), 1034-1052
• ABRAHART, RJ and SEE, LM, 2007. Neural Network Modelling Of Non-Linear Hydrological Relationships Hydrology And Earth System Sciences. 11(5), 1563-1579
• SEE, L, SOLOMATINE, D, ABRAHART, R and TOTH, E, 2007. Hydroinformatics: Computational Intelligence And Technological Developments In Water Science Applications - Editorial
• DAWSON, C.W., ABRAHART, R.J., SHAMSELDIN, A.Y. and WILBY, R.L., 2006. Flood estimation at ungauged sites using artificial neural networks Journal of Hydrology. VOL 319(NUMBER 1-4), 391-409
• DAWSON, C.W., SEE, L.M., ABRAHART, R.J. and HEPPENSTALL, A.J., 2006. Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England Neural Networks. VOL 19(NUMBER 2), 236-247
• KNEALE, P.E., SEE, L.M. and ABRAHART, R.J., 2004. What use is a neural network?. In: , ed., Neural Networks for Hydrological Modelling 15-38
• ABRAHART, R.J., SEE, L.M. and KNEALE, P.E., 2004. Towards a hydrological research agenda. In: , ed., Neural Networks for Hydrological Modelling 289-304
• ABRAHART, R.J., 2004. Neural network modelling: basic tools and broader issues. In: , ed., Neural Networks for Hydrological Modelling 289-304
• ABRAHART, R.J., 2003. Neural network rainfall-runoff forecasting based on continuous resampling Journal of Hydroinformatics. 5(1), 51-62
• WILBY, R.L., ABRAHART, R.J. and DAWSON, C.W., 2003. Detection of conceptual model rainfall-runoff processes inside an artificial neural network Hydrological Sciences Journal. VOL 48(PART 2), 163-182
• ABRAHART, R.J. and SEE, L.M., 2002. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments Hydrology and Earth System Sciences. 6(4), 655-670
• EHLEN, J. and ABRAHART, R.J., 2002. Effective Use of Terrain in the American Civil War: the Battle of Fredericksburg, December 1862
• KIRBY, M.J., ABRAHART, R.J., BATHURST, J.C., MCMAHON, M.L., OSBORNE, C.P., THORNES, J.B. and WOODWARD, F.I., 2002. MEDRUSH: a Basin-scale Physically Based Model for Forecasting Runoff and Sediment Yield. In: Mediterranean Desertification
• ABRAHART, B, 2002. Neural Networks - Preface: The End Of The Beginning
• ABRAHART, R.J., SEE, L.M. and KNEALE, P.E., 2001. Investigating the role of saliency analysis with a neural network rainfall-runoff model Computers and Geosciences. 27(8), 921-928
• SEE, L.M. and ABRAHART, R.J., 2001. Multi-model data fusion for hydrological forecasting Computers and Geosciences. VOL 27(NUMBER 8), 987-994
• ABRAHART, R.J. and WHITE, S.M., 2000. Modelling Sediment Transfer in Malawi: Comparing Backpropagation Neural Network Solutions Against a Multiple Linear Regression Benchmark Using Small Data Sets Physics and Chemistry of the Earth Part B: Hydrology, Oceans & Atmosphere. VOL 26(NUMBER 1), 19-24
• ABRAHART, R.J. and SEE, L.M., 2000. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contracting catchments Hydrological Processes: An International Journal. 14(11-12), 2157-2172
• FISCHER, M.M. and ABRAHART, R.J., 2000. Neurocomputing - Tools for Geographers. In: Geocomputation 187-217
• EHLEN, J. and ABRAHART, R.J., 2000. Effective Use of Terrain: the Battle of Fredericksburg, December 1862 In: Terrain in Military History.
• ABRAHART, R.J., 1999. Neurohydrology: implementation options and a research agenda Area. VOL 31(NUMBER 2), 141-150
• ABRAHART, R.J., SEE, L.M. and KNEALE, P.E., 1999. Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural rainfall-runoff model Journal of Hydroinformatics. 1(2), 103-114
• KIRKBY, M.J., ABRAHART, R.J., MCMAHON, M.D., SHAO, J. and THORNES, J.B., 1998. MEDALUS soil erosion models for global change Geomorphology. VOL 24(ISSUE 1), 35-49
• FISHER, P., ABRAHART, R. J. and HERBINGER, W., 1997. The Sensitivity of Two Distributed Non-point Source Pollution Models to the Spatial Arrangement of the Landscape Hydrological Processes: An International Journal. VOL 11(NUMBER 3), 241-252