River flow time series prediction with a range-dependent neural network
Open Access
- 1 October 2001
- journal article
- research article
- Published by Informa UK Limited in Hydrological Sciences Journal
- Vol. 46 (5), 729-745
- https://doi.org/10.1080/02626660109492867
Abstract
Artificial neural networks provide a promising alternative to hydrological time series modelling. However, there are still many fundamental problems requiring further analyses, such as structure identification, parameter estimation, generalization, performance improvement, etc. Based on a proposed clustering algorithm for the training pairs, a new neural network, namely the range-dependent neural network (RDNN) has been developed for better accuracy in hydrological time series prediction. The applicability and potentials of the RDNN in daily streamflow and annual reservoir inflow prediction are examined using data from two watersheds in China. Empirical comparisons of the predictive accuracy, in terms of the model efficiency R2 and absolute relative errors (ARE), between the RDNN, back-propagation (BP) networks and the threshold auto-regressive (TAR) model are made. The case studies demonstrated that the RDNN network performed significantly better than the BP network, especially for reproducing low-flow events.Keywords
This publication has 46 references indexed in Scilit:
- Daily reservoir inflow forecasting using artificial neural networks with stopped training approachJournal of Hydrology, 2000
- Artificial Neural Networks in Hydrology. I: Preliminary ConceptsJournal of Hydrologic Engineering, 2000
- Artificial Neural Networks in Hydrology. II: Hydrologic ApplicationsJournal of Hydrologic Engineering, 2000
- River flood forecasting with a neural network modelWater Resources Research, 1999
- A comparison between neural-network forecasting techniques-case study: river flow forecastingIEEE Transactions on Neural Networks, 1999
- An artificial neural network approach to rainfall-runoff modellingHydrological Sciences Journal, 1998
- Neural modeling for time series: A statistical stepwise method for weight eliminationIEEE Transactions on Neural Networks, 1995
- Rainfall forecasting in space and time using a neural networkJournal of Hydrology, 1992
- Improving generalization performance using double backpropagationIEEE Transactions on Neural Networks, 1992
- Successive Linear Programming at ExxonManagement Science, 1985