Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source
3. Methods
3.1. GRU Neural Network Model
3.2. Construction of GRU Model for Medium- and Long-term SST Prediction
3.3. Data Preprocessing
4. Results and Discussion
4.1. Experiment Setup
4.2. Results of Using Different Parameters
4.3. Monthly Data Prediction
4.4. Quarterly Data Prediction
5. Conclusions
- (1)
- The designed SST prediction model based on GRU can efficiently fit the trend of the real SST and has high reliability. Additionally, the proposed model in this paper has the characteristics of a conservative estimation for SST prediction; that is, the predicted value of SST is smaller than the real value. Furthermore, LSTM experiences the same problem as the proposed method in this paper.
- (2)
- In the prediction at the monthly time scale, RMSE and MAE are mostly concentrated in the range of 0–2.5 °C. In the prediction at the quarterly time scale, RMSE and MAE are mostly concentrated in the range of 0–2 °C. The r values of both time scales are above 0.98, indicating high prediction accuracy.
- (3)
- The proposed prediction model has more stable results than LSTM. The advantages are more evident with an increase in prediction length.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site | Month | Quarter | |||||
---|---|---|---|---|---|---|---|
Mean | Coldest | Warmest | Std.dev | Coldest | Warmest | Std.dev | |
P1 | 13.12 | −0.44 | 27.05 | 8.48 | 2.18 | 24.09 | 7.69 |
P2 | 12.99 | 0.22 | 26.51 | 8.16 | 2.65 | 23.13 | 7.46 |
P3 | 13.59 | −0.31 | 28.59 | 8.90 | 1.83 | 26.06 | 8.09 |
P4 | 12.86 | −0.22 | 26.57 | 8.33 | 2.11 | 23.36 | 7.62 |
P5 | 13.13 | 0.66 | 26.79 | 7.86 | 3.13 | 23.21 | 7.19 |
P6 | 12.53 | 0.26 | 25.82 | 7.95 | 2.75 | 22.66 | 7.29 |
Site | Pre_4m 1 | Pre_6m 1 | Pre_12m 1 | Pre_18m 1 | Pre_24m 1 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | ||
LSTM | P1 | 0.631 | 0.567 | 1.345 | 1.176 | 1.603 | 1.373 | 1.386 | 1.108 | 2.881 | 2.238 |
P2 | 0.324 | 0.263 | 1.670 | 1.512 | 2.189 | 1.908 | 2.097 | 4.389 | 9.954 | 3.440 | |
P3 | 1.000 | 0.937 | 1.742 | 1.582 | 1.772 | 1.486 | 1.973 | 1.736 | 3.785 | 2.644 | |
P4 | 0.616 | 0.545 | 1.423 | 1.183 | 4.248 | 3.288 | 1.958 | 1.750 | 1.986 | 1.494 | |
P5 | 0.517 | 0.457 | 2.298 | 1.929 | 1.899 | 1.565 | 1.496 | 1.247 | 1.775 | 1.402 | |
P6 | 0.231 | 0.200 | 1.711 | 1.384 | 1.466 | 1.246 | 2.645 | 2.060 | 1.328 | 0.986 | |
Proposed model | P1 | 1.362 | 1.286 | 0.598 | 0.552 | 1.247 | 1.168 | 1.112 | 1.026 | 1.613 | 1.275 |
P2 | 0.653 | 0.569 | 0.717 | 0.570 | 1.258 | 1.027 | 1.129 | 1.275 | 3.196 | 1.124 | |
P3 | 0.677 | 0.539 | 1.370 | 1.261 | 1.128 | 0.933 | 1.645 | 1.461 | 2.891 | 2.353 | |
P4 | 1.205 | 1.160 | 1.216 | 1.061 | 1.293 | 1.153 | 1.271 | 1.018 | 1.419 | 1.083 | |
P5 | 0.862 | 0.807 | 1.089 | 0.908 | 1.256 | 1.044 | 1.355 | 1.158 | 1.636 | 1.286 | |
P6 | 1.155 | 1.106 | 1.159 | 1.477 | 0.929 | 0.810 | 1.676 | 1.294 | 1.167 | 0.991 |
Location | Pre_2q 1 | Pre_4q 1 | Pre_6q 1 | Pre_8q 1 | |||||
---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | ||
LSTM | P1 | 1.676 | 1.636 | 2.150 | 2.023 | 2.810 | 3.045 | 3.482 | 3.336 |
P2 | 1.964 | 1.842 | 3.907 | 3.513 | 2.276 | 2.315 | 2.980 | 2.865 | |
P3 | 3.006 | 2.334 | 2.829 | 2.039 | 2.175 | 2.042 | 3.426 | 2.711 | |
P4 | 2.698 | 2.683 | 2.292 | 1.975 | 1.245 | 0.993 | 1.748 | 1.496 | |
P5 | 1.578 | 1.325 | 2.751 | 2.433 | 0.727 | 0.699 | 1.395 | 1.025 | |
P6 | 1.856 | 1.587 | 2.214 | 1.882 | 1.593 | 1.737 | 2.415 | 2.264 | |
Proposed model | P1 | 1.593 | 1.575 | 2.153 | 1.848 | 1.86 | 1.976 | 1.577 | 1.305 |
P2 | 1.636 | 1.636 | 1.628 | 1.545 | 1.536 | 1.553 | 1.948 | 1.743 | |
P3 | 2.239 | 1.955 | 0.993 | 0.794 | 1.692 | 1.679 | 3.423 | 2.896 | |
P4 | 2.302 | 2.165 | 1.673 | 1.267 | 0.937 | 0.909 | 0.892 | 0.687 | |
P5 | 1.326 | 1.012 | 2.635 | 2.393 | 0.714 | 0.705 | 1.049 | 0.883 | |
P6 | 1.504 | 1.153 | 1.870 | 1.763 | 1.135 | 1.126 | 1.600 | 1.275 |
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Zhang, Z.; Pan, X.; Jiang, T.; Sui, B.; Liu, C.; Sun, W. Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. J. Mar. Sci. Eng. 2020, 8, 249. https://doi.org/10.3390/jmse8040249
Zhang Z, Pan X, Jiang T, Sui B, Liu C, Sun W. Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. Journal of Marine Science and Engineering. 2020; 8(4):249. https://doi.org/10.3390/jmse8040249
Chicago/Turabian StyleZhang, Zhen, Xinliang Pan, Tao Jiang, Baikai Sui, Chenxi Liu, and Weifu Sun. 2020. "Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network" Journal of Marine Science and Engineering 8, no. 4: 249. https://doi.org/10.3390/jmse8040249
APA StyleZhang, Z., Pan, X., Jiang, T., Sui, B., Liu, C., & Sun, W. (2020). Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. Journal of Marine Science and Engineering, 8(4), 249. https://doi.org/10.3390/jmse8040249