Baseflow separation techniques for modular artificial neural network modelling in flow forecasting
暂无分享,去创建一个
[1] F. R. Hall. Base‐Flow Recessions—A Review , 1968 .
[2] M. J. HALL,et al. Hydrology for Engineers , 1969, Nature.
[3] Peter K. Kitanidis,et al. Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .
[4] Fionn Murtagh,et al. Cluster Dissection and Analysis: Theory, Fortran Programs, Examples. , 1986 .
[5] Daniel N. Osherson,et al. Modular learning , 1993 .
[6] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .
[7] Ashok N. Srivastava,et al. Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..
[8] Peter M. Allen,et al. Automated Base Flow Separation and Recession Analysis Techniques , 1995 .
[9] Ronald A. Sloto,et al. HYSEP: A Computer Program for Streamflow Hydrograph Separation and Analysis , 1996 .
[10] R. McCuen. Hydrologic Analysis and Design , 1997 .
[11] A. Geva. Non-stationary time-series prediction using fuzzy clustering , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).
[12] D. Solomatine,et al. Automatic calibration of groundwater models using global optimization techniques , 1999 .
[13] Yonas B. Dibike,et al. Application of artificial neural networks to the simulation of a two dimensional flow , 1999 .
[14] Stan Openshaw,et al. A hybrid multi-model approach to river level forecasting , 2000 .
[15] V. Isham,et al. Design of the HYREX raingauge network , 2000 .
[16] null null,et al. Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .
[17] Asaad Y. Shamseldin,et al. A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .
[18] Robert J. Abrahart,et al. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments , 2002 .
[19] Armando Brath,et al. Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models , 2002 .
[20] S. Uhlenbrook,et al. Hydrograph separations in a mesoscale mountainous basin at event and seasonal timescales , 2002 .
[21] Tom G. Chapman,et al. Modelling stream recession flows , 2003, Environ. Model. Softw..
[22] R. Abrahart,et al. Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .
[23] D. Solomatine,et al. Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .
[24] Charles Audet,et al. Generalized pattern searches with derivative information , 2002, Math. Program..
[25] M. J. Hall,et al. Rainfall-Runoff Modelling , 2004 .
[26] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[27] Dimitri P. Solomatine,et al. M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .
[28] D.P. Solomatine,et al. Semi-optimal hierarchical regression models and ANNs , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[29] F. Anctil,et al. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition , 2004 .
[30] Dong-Jun Seo,et al. Towards the characterization of streamflow simulation uncertainty through multimodel ensembles , 2004 .
[31] J.J. Valdes,et al. Time dependent neural network models for detecting changes of state in Earth and planetary processes , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[32] Holger R. Maier,et al. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river , 2005 .
[33] K. Eckhardt. How to construct recursive digital filters for baseflow separation , 2005 .
[34] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[35] Holger R. Maier,et al. Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .
[36] A.N. Belbachir,et al. A comparative study of artificial neural network techniques for river stage forecasting , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[37] P. Gelder,et al. Forecasting daily streamflow using hybrid ANN models , 2006 .
[38] N. Lauzon,et al. Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts , 2006 .
[39] Ashu Jain,et al. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques , 2006 .
[40] Dimitri P. Solomatine,et al. Optimal modularization of learning models in forecasting environmental variables , 2006 .
[41] Dimitri P. Solomatine,et al. Learning hydrologic flow separation algorithm and local ANN committee modeling , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[42] Michael Hammond,et al. 7th International Conference on Hydroinformatics, Nice, France , 2006 .