Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods
Authors:
- Gwo-Fong LinGLGwo-Fong Lin
- Guo-Rong ChenGCGuo-Rong Chen
- Pei-Yu HuangPHPei-Yu Huang
- Yang-Ching ChouYCYang-Ching Chou
- 1 June 2009
- journal article
- Published by Elsevier in Journal of Hydrology
- Vol. 372 (1-4) , 17-29
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- A systematic approach to the input determination for neural network rainfall–runoff modelsHydrological Processes, 2007
- Forecasting of hydrologic time series with ridge regression in feature spaceJournal of Hydrology, 2007
- Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles FlouesHydrological Sciences Journal, 2005
- Application of an artificial neural network to typhoon rainfall forecastingHydrological Processes, 2005
- Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curvesHydrological Processes, 2004
- State space neural networks for short term rainfall-runoff forecastingJournal of Hydrology, 2004
- A non-linear rainfall-runoff model using radial basis function networkJournal of Hydrology, 2004
- Short‐term inflow forecasting using an artificial neural network modelHydrological Processes, 2002
- FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1Jawra Journal of the American Water Resources Association, 2002
- Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applicationsEnvironmental Modelling & Software, 2000