Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation
- 1 July 2005
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Hydrologic Engineering
- Vol. 10 (4), 336-341
- https://doi.org/10.1061/(asce)1084-0699(2005)10:4(336)
Abstract
The majority of artificial neural network (ANN) applications to water resources data employ the feed-forward back-propagation (FFBP) method. This study used an ANN algorithm, the generalized regression neural network (GRNN), for intermittent river flow forecasting and estimation. GRNNs were superior to FFBP in terms of the selected performance criteria. The GRNN simulations do not face the frequently encountered local minima problem in FFBP applications, and GRNNs do not generate forecasts or estimates that are not physically plausible. Preliminary analysis of statistics such as auto- and cross correlation, which explained variance by multilinear regression and the Akaike criterion for the autoregressive moving average (ARMA) model of corresponding order, were found quite informative in determining the number of nodes in the input layer of neural networks.Keywords
This publication has 21 references indexed in Scilit:
- Flow prediction by three back propagation techniques using k-fold partitioning of neural network training dataHydrology Research, 2005
- Discussion of “Performance of Neural Networks in Daily Streamflow Forecasting” by S. Birikundavyi, R. Labib, H. T. Trung, and J. RousselleJournal of Hydrologic Engineering, 2004
- Estimation and forecasting of daily suspended sediment data by multi-layer perceptronsAdvances in Water Resources, 2004
- Estimation, forecasting and extrapolation of river flows by artificial neural networksHydrological Sciences Journal, 2003
- Incorporation of ARMA models into flow forecasting by artificial neural networksEnvironmetrics, 2003
- Bivariate stochastic modelling of ephemeral streamflowHydrological Processes, 2002
- Estimation of missing streamflow data using principles of chaos theoryJournal of Hydrology, 2002
- Hydrological modelling using artificial neural networksProgress in Physical Geography: Earth and Environment, 2001
- Artificial Neural Networks in Hydrology. I: Preliminary ConceptsJournal of Hydrologic Engineering, 2000
- Artificial Neural Networks in Hydrology. II: Hydrologic ApplicationsJournal of Hydrologic Engineering, 2000