Discussion of 'Reservoir Computing approach to Great Lakes water level forecasting' by P. Coulibaly [J. Hydrol. 381(2010) 76-88]

Journal of Hydrology

Volumn 422-423, Issue , 2012, Pages 76-80

  Abrahart, Robert J ^a   Mount, Nick J ^a   Shamseldin, Asaad Y ^b  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

Echo state network; Forecasting; Great lakes; Linear benchmarking; Reservoir computing; Water levels

Indexed keywords

EID: 84856404995     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2011.10.006     Document Type: Note

References (5)

1. Abrahart R.J., See L.M. Neural network modelling of non-linear hydrological relationships. Hydrol. Earth Syst. Sci. 2007, 11(5):1563-1579.
2. Coulibaly P. Reservoir computing approach to Great Lakes water level forecasting. J. Hydrol. 2010, 381(1-2):76-88.
3. Jaeger, H., 2001.The Echo State Approach to Analyzing and Training Recurrent Neural Networks. Technical Report. GMD Report No. 148.German National Research Center for Information Technology, Bremen.
4. Ljung L. System Identification: Theory for the User 1999, Prentice Hall, New Jersey. second ed.
5. Mount N.J., Abrahart R.J. Discussion of 'River flow estimation from upstream flow records by artificial intelligence methods' by M.E. Turan and M.A. Yurdusev [J. Hydrol. 369 (2009) 71-77]. Journal of Hydrology 2011, 396(1-2):193-196.