Forecasts of tropical Pacific sea surface temperatures by neural networks and support vector regression
Nonlinear and linear regression models were developed to forecast the sea surface temperature anomalies (SSTA) across the tropical Pacific ocean. The methods used were, Bayesian neural networks (BNN), support vector machines for regression (SVR) and linear regression (LR). The predictors of the mod...
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| Format: | Others |
| Language: | English |
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University of British Columbia
2009
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| Online Access: | http://hdl.handle.net/2429/7577 |
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