Hall, Mike J.

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Photograph[edit]

photo required for Mike Hall


Dates[edit]

Michael John Hall 1942 - 2005

Biography[edit]

On 26 April 2005 we were saddened to hear of the death of Michael John Hall, Emeritus Professor of Hydrology at UNESCO-IHE. Mike will be remembered by many in the Hydroinformatics community through his groundbreaking work together with Tony Minns in the field of neural networks and rainfall-runoff modelling. Mike started his career in hydrology with his PhD from Imperial College in 1967 entitled Artificial Rainfall in Laboratory Hydrology. He joined Sir William Halcrow & Partners in 1976 as Principal Hydrologist until his return to academia in 1986 as Professor and Head of School of Civil Engineering at Middlesex Polytechnic (now Middlesex University). Mike joined IHE-Delft (now UNESCO-IHE) in 1992 as Professor of Hydrology and remained in Delft until his retirement in December 2003. It was devastating that at this same moment Mike’s long and unfortunately unsuccessful battle against cancer began. Mike returned to the UK in March 2005, when he was also awarded Honorary Membership of the British Hydrological Society.

Hydrological Achievements[edit]

Mike Hall worked on the use of laboratory sprinkling systems to study the hydrological catchments which led to a particular interest in urban hydrology resulting in his well known book on the subject. With Tony Minns he was amongst the first to apply neural network concepts in hydrological modelling.

The introduction of Hydroinformatics in the early 1990s brought the promise of new techniques to hydraulics and hydrology with new opportunities for modelling certain physical relations that had, to date, been considered too difficult to model using traditional methods. Through their interaction at IHE, Mike and Tony began exploring the application of neural networks to the problem of rainfall- runoff modelling. Their first joint paper in 1993 for the British Hydrological Society introduced some of the basic concepts needed to be addressed and it served to “test the waters” of the hydrology world for this new concept. It was probably only because of the great respect and admiration that Mike had amongst his colleagues in this society that he was given the chance to challenge the audience with some controversial findings. Mike’s immense knowledge and familiarity with the most basic hydrological processes made it possible to address the criticisms much to the satisfaction of the early sceptics. The early experiments involved the use of the artificial laboratory catchment data that Mike had collected so many years before. After much debate, discussion and letter writing, Mike and Tony finally succeeded in getting a paper published in a refereed hydrology journal in 1996, and this is still serves as one of the fundamental papers on this subject.

In later years, Mike continued to work on some of the more illusive problems such as flood frequency analysis on ungauged catchments and the inscrutable problem of extrapolation using neural networks. Much of this latter work was carried out by MSc and PhD students at IHE. Mike’s immense knowledge of hydrology and hydrological processes has meant that this research has always been founded upon fundamental hydrological principals and the results are not just presented as just another “black-box” approach.

Reference Material[edit]

Source: Obituary in Journal of Hydroinformatics, 7.4, 2005

Major Publications[edit]

Books

  • Hall, M. J., 1984, Urban hydrology, Elsevier Applied Science Publishers: London, New York ISBN 0853342687
  • E.R. Dahmen M.J. Hall, 1990, Screening of Hydrological Data: Tests for Stationarity and Relative Consistency, Report No. 49, International Institute for Land Reclamation and Improvement/ILRI, Wageningen, The Netherlands

Papers

  • Hall, M.J. and Minns, A.W., 1993, September. Rainfall-runoff modelling as a problem in artificial intelligence: experience with a neural network. In BHS 4th National Hydrology Symposium, Cardiff (pp. 5-51).
  • Minns, A.W. and Hall, M.J., 1996. Artificial neural networks as rainfall-runoff models. Hydrological sciences journal, 41(3), pp.399-417.
  • Minns, A.W. and Hall, M.J., 1997, Living with the ultimate black box: more on artificial neural networks. In BHS 6th National Hydrology Symposium (pp. 39-45).
  • Hall, M.J. and Minns, A.W., 1999. The classification of hydrologically homogeneous regions. Hydrological Sciences Journal, 44(5), pp.693-704.
  • Hall, M.J., 2001. How well does your model fit the data?. Journal of Hydroinformatics, 3(1), pp.49-55.
  • Hall, M.J., Zaki, A.F. and Shahin, M.M.A., 2001. Regional analysis using the geomorphoclimatic instantaneous unit hydrograph. Hydrology and Earth System Sciences Discussions, 5(1), pp.93-102.
  • Hall, M.J., Minns, A.W. and Ashrafuzzaman, A.K.M., 2002. The application of data mining techniques for the regionalisation of hydrological variables. Hydrology and Earth System Sciences Discussions, 6(4), pp.685-694.
  • MINNS, A.W. and HALL, M.J., 2004. Rainfall-runoff modelling. in Neural networks for hydrological modelling. London, pp.157-175.
  • Jingyi, Z. and Hall, M.J., 2004. Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology, 296(1), pp.98-117.
  • Minns, A.W. and Hall, M.J., 2005. Artificial neural network concepts in hydrology. Encyclopedia of Hydrological Sciences.
  • Hettiarachchi, P., Hall, M.J. and Minns, A.W., 2005. The extrapolation of artificial neural networks for the modelling of rainfall—runoff relationships. Journal of Hydroinformatics, 7(4), pp.291-296.

Links[edit]