Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model
Abstract
:1. Introduction
2. Methodology
2.1. Dataset
2.2. Recurrent Learning
2.3. Divide and Conquer Prediction Model of the Gom SSH
2.4. Sinusoidal-Weighted Overlapped Partitioning
3. LSTM Forecasting of the Gom SSH with Overlapping Partitions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
DAC | Divide and Conquer |
SSH | Sea Surface Height |
LC | Loop Current |
LCS | Loop Current System |
GoM | Gulf of Mexico |
RNN | Recurrent Neural Network |
LSTM | Long Short-Term Memory Network |
PC | Principal Components |
EOF | Empirical Orthogonal Functions |
RMSE | Root Mean Squared Error |
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Wang, J.L.; Zhuang, H.; Chérubin, L.; Muhamed Ali, A.; Ibrahim, A. Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model. Forecasting 2021, 3, 570-579. https://doi.org/10.3390/forecast3030036
Wang JL, Zhuang H, Chérubin L, Muhamed Ali A, Ibrahim A. Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model. Forecasting. 2021; 3(3):570-579. https://doi.org/10.3390/forecast3030036
Chicago/Turabian StyleWang, Justin L., Hanqi Zhuang, Laurent Chérubin, Ali Muhamed Ali, and Ali Ibrahim. 2021. "Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model" Forecasting 3, no. 3: 570-579. https://doi.org/10.3390/forecast3030036