Name
Abstract | ||
|---|---|---|
This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to incre... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TGRS.2017.2659538 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Predictive models,Discrete wavelet transforms,Kernel,Training,Optimization | Kernel (linear algebra),Bat algorithm,Support vector machine,Robustness (computer science),Artificial intelligence,Operator (computer programming),Univariate,Machine learning,Mathematics,Wavelet transform,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
55 | 6 | 0196-2892 |
Citations | PageRank | References |
2 | 0.39 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdollah Kavousi-Fard | 1 | 268 | 31.99 |
| Wencong Su | 2 | 254 | 27.89 |