Name
Title | ||
|---|---|---|
PSO optimizing neural network for the Yangtze river sediment entering estuary prediction |
Abstract | ||
|---|---|---|
The artificial neural network method is used to study the sediment entering the estuary prediction in the Yangtze River. Particle swarm optimization is applied to optimize the node numbers of the hidden layers in the ANN model and overcome the over-fitting problem. Datong hydrological station is the control station as the sediment entering the estuary. Based on the monitoring sediment load data of from 1956 to 2005 year, PSORBF neural network was applied to predict river sediment. The study indicates that the model is practical and has better prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICNC.2010.5584412 | ICNC |
Keywords | Field | DocType |
sediments,artificial neural network method,over-fitting problem,the yangtze river,pso,particle swarm optimisation,rivers,particle swarm optimzation,environmental science computing,sediment load,particle swarm optimization,yangtze river sediment,psorbf neural network,datong hydrological station,artificial neural network,estuary prediction,neural nets,neural network,predictive models,water resources,artificial neural networks | Particle swarm optimization,Sediment,Mathematical optimization,Computer science,Estuary,Water resources,Artificial neural network,Marine engineering | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-5958-2 | 1 |
PageRank | References | Authors |
0.52 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxian Guo | 1 | 1 | 0.86 |
| Hongxiang Wang | 2 | 6 | 6.12 |