Ideal point error for model assessment

Introduction Conclusions References

[1]  S. Wȩglarczyk,et al.  The interdependence and applicability of some statistical quality measures for hydrological models , 1998 .

[2]  Keith Beven,et al.  Distributed Hydrological Modelling , 1998 .

[3]  Robert J. Abrahart,et al.  HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts , 2007, Environ. Model. Softw..

[4]  T. McMahon,et al.  Assessing the adequacy of catchment streamflow yield estimates , 1993 .

[5]  Suwanee Boonmung,et al.  Evaluation of artificial neural networks for pineapple grading , 2006 .

[6]  Christian W. Dawson,et al.  Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze , China 619 , 2002 .

[7]  C.W. Dawson,et al.  HydroTest: Further development of a web resource for the standardised assessment of hydrological models , 2010, Environ. Model. Softw..

[8]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[9]  M. Masmoudi,et al.  The performance of some real-time statistical flood forecasting models seen through multicriteria analysis , 1993 .

[10]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology , 2009 .

[11]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[12]  Robert J. Abrahart,et al.  The search for orthogonal hydrological modelling metrics: a case study of 20 monitoring stations in Colombia , 2011 .

[13]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[14]  Patrick Willems,et al.  A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models , 2009, Environ. Model. Softw..

[15]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application , 2009 .

[16]  Hoshin Vijai Gupta,et al.  Do Nash values have value? , 2007 .

[17]  Robert J. Abrahart,et al.  Neural network modelling of non-linear hydrological relationships , 2007 .

[18]  Robert E. Criss,et al.  Do Nash values have value? Discussion and alternate proposals , 2008 .

[19]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[20]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[21]  Kayamkhani Taher Abbasbhai,et al.  Evaluation of Artificial Neural Networks , 2012 .

[22]  M. J. Hall How well does your model fit the data , 2001 .

[23]  Robert J. Abrahart,et al.  Discussion of “River flow estimation from upstream flow records by artificial intelligence methods” by M.E. Turan, M.A. Yurdusev [J. Hydrol. 369 (2009) 71-77] , 2011 .

[24]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[25]  Patrick Willems,et al.  Model uncertainty analysis by variance decomposition , 2010 .

[26]  A. Elshorbagy,et al.  Performance Evaluation of Artificial Neural Networks for Runoff Prediction , 2000 .