Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach
- 15 April 2009
- journal article
- research article
- Published by Elsevier BV in Journal of Hydrology
- Vol. 367 (3-4), 165-176
- https://doi.org/10.1016/j.jhydrol.2008.10.019
Abstract
No abstract availableKeywords
This publication has 26 references indexed in Scilit:
- Input determination for neural network models in water resources applications. Part 1—background and methodologyJournal of Hydrology, 2005
- Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a riverJournal of Hydrology, 2005
- Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identificationJournal of Hydrology, 2000
- Radial Basis Function Network Configuration Using Mutual Information and the Orthogonal Least Squares AlgorithmNeural Networks, 1996
- The Use of Artificial Neural Networks for the Prediction of Water Quality ParametersWater Resources Research, 1996
- The Identification of Multiple OutliersJournal of the American Statistical Association, 1993
- Kernel flood frequency estimators: Bandwidth selection and kernel choiceWater Resources Research, 1993
- Optimum operation of a multiple reservoir system including salinity effectsWater Resources Research, 1992
- A general regression neural networkIEEE Transactions on Neural Networks, 1991
- On optimal and data-based histogramsBiometrika, 1979