References

Useful references and some sources of the statistics used:

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Akaike, H. (1974) ‘A new look at the statistical model identification’, IEEE Transactions on Automotive Control, AC-19, pp 716 – 723.

Appolov, B., Kalinin G., Komarov, V.  (1974): Hydrological forecasting course, Guidrometeoizdat, Leningrad, 419 p. (In Russian)

Armstrong, J.S. and Collopy, F. (1992) ‘Error measures for generalizing about forecasting methods: Empirical comparisons’, Journal of Forecasting, Vol 8, pp 69 – 80.

Astatkie, T. (2006) 'Absolute and relative measures for evaluating the forecasting performance of time series models for daily streamflows', Nordic Hydrology, Vol 37(3), pp 205 - 215.

ASCE (1993) ‘Criteria for evaluation of watershed models’, Journal of Irrigation and Drainage Engineering, Vol 119(3), pp 429 – 442.

Beran, M. (1999) ‘Hydrograph prediction – how much skill?’, Hydrology and Earth System Sciences, Vol 3(2), pp 305 – 307.

Criss, R.E. and Winston, W.E. (2008) 'Do Nash values have value? Discussion and alternate proposals', Hydorlogical Processes, Vol 22, pp 2723 - 2725.

Dawson, C.W. and Wilby, R.L. (2001) 'Hydrological modelling using artificial neural networks', Progress in Physical Geography, 0309-1333, Vol 25(1), pp 80 - 108.

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 and Software, Vol 22, pp 1034 - 1052.

deVos, N.J. and Rientjes, T.H.M. (2005) ‘Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation’, Hydrology and Earth System Sciences, Vol 9, pp 111 – 126.

de Vos, N.J., Rientjes, T.H.M. (2007) 'Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model', Hydroloigcal Sciences Journal, Vol 52(3), pp 397 - 413.

Garrick, M., Cunnane, C., Nash, J.E. (1978) ‘A criterion of efficiency for rainfall-runoff models’, Journal of Hydrology, Vol 36, pp 375-381.

Gupta, H.V. Sorooshian, S. and Yapo, P.O. (1998) 'Toward improved calibration of hydrological models: Multiple and noncommensurable measures of information, Water Resources Research, Vol 34(4), pp 751 - 763.

Gupta, H.V. Kling, H. Yilmaz, K.K. and Martinez, G.F. (2009) 'Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling', Journal of Hydrology, Vol 377, pp 80-91.

Hall, M.J. (2001) 'How well does your model fit the data?', Journal of Hydroinformatics, Vol 3(1), pp 49 - 55.

Huang, W.C. and Yang, F.T. (2005) ‘A study on regionalized hydrologic model’, < http://wrm.hre.ntou.edu.tw/wrm/sci/water10th/>, (accessed 29 September, 2005)

Jain, A. and Srinivasulu, S. (2004) 'Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques', Water Resources Research, Vol 40, W04302.

Kanji, G.K. (1993) '100 Statistical Tests', Sage Publications, London.

Karunanithi, N., Grenney, W., Whitley, D., Bovee, K. (1994) ‘Neural networks for river flow prediction’, Journal of Computing in Civil Engineering, Vol 8, pp. 201 – 220.

Kitanidis, P.K. and Bras, R.L. (1980) 'Real-time forecasting with a conceptual hydrologic model: 2. Application and results', Water Resources Research, Vol 16, pp 1034 - 1044.

Kumar, A.R.S. Sudheer, K.P. Jain, S.K. and Agarwal, P.K. (2005) 'Rainfall-runoff modelling using artificial neural networks: comparison of network types', Hydrological Processes, Vol 19, pp 1277 - 1291.

Legates, D.R. and McCabe, G.J. (1999) 'Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation', Water Resources Research, Vol 35(1), pp 233 - 241.

Lin, G.F. and Chen, L.H. (2005) 'Time series forecasting by combining the radial basis function network and self-organizing map', Hydrological Processes, Vol 19, pp 1925 - 1937.

Lorrai, M. and Sechi G. (1995) ‘Neural nets for modelling rainfall-runoff transformations’, Water Resources Management, Vol 9, pp 299 - 313.

Madsen, H. (2000) 'Automatic calibration of a conceptual rainfall-runoff model using multiple objectives', Journal of Hydrology, Vol 235, pp 276 - 288.

Nash, J.E. and Sutcliffe, J.V. (1970) ‘River flow forecasting through conceptual models 1: A discussion of principles’, Journal of Hydrology, Vol 10, pp 282 – 290.

Popov E.G. (1968): Fundaments of hydrological  forecasting, Guidrometeorologuicheskoie izdatielztvo, Leningrad, 294 p. (in Russian)

Rissanen, J. (1978) ‘Modeling by short data description’, Automation, Vol 14, pp 465 – 471.

Seibert, J. (2001) ‘On the need for benchmarks in hydrological modelling’, Hydrological Processes, Vol 15, pp 1063 – 1064.

Shamseldin, A.Y. (1997) ‘Application of a neural network technique to rainfall-runoff modelling’, Journal of Hydrology, Vol 199, pp 272 – 294.

Teegavarapu, R. and Elshorbagy, A. (2005) ‘Fuzzy set based error measure for hydrologic model evaluation’, Journal of Hydroinformatics, Vol 7(3), pp 199 – 207.

Watts, G. (1997) ‘Hydrological modelling in practice’, in Contemporary Hydrology: Towards holistic environmental science, Wilby, R.L. (ed), John Wiley, UK.

Wilmott, C.J. (1981) ‘On the validation of models’, Physical Geography, Vol 2, pp 184 – 194.

   
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