Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Methods
2.2.1. LSTM Neural Network
2.2.2. Model Building
- (1)
- Network initialization. Weights vector W and bias vector b are randomly initialized. The initial learning rate and the maximum number of iterations are set to 0.0001 and 100, respectively, where EarlyStopping is used in the number of iterations.
- (2)
- Data standardization. The missing values in the data are filled with the surrounding values, and the MinmaxScaler function is imported from the sklearn library to standardize the dataset X to (−1, 1) to obtain the standardized dataset X.
- (3)
- The division of dataset X. The standardized dataset X is set according to the window length L and the number of days of prediction, in which the training set and the validation set are divided into 85% and 15%, respectively.
- (4)
- Error calculation. The error between the output of the output layer and the satellite data and the loss function are calculated using MSE.
- (5)
- Update of weights and thresholds. Using the Adam gradient optimization algorithm, update the weights W and biases b according to the loss function.
- (6)
- Repeat steps (3) to (5). The training ends when the training times reach the maximum number of iterations, or the value of the loss function does not change for three consecutive iterations.
2.2.3. Evaluation Indicators
3. Results
3.1. The Effect of Different Parameter Settings on LSTM Prediction Performance
3.1.1. The Impact of Input Length on LSTM Prediction Performance
3.1.2. The Impact of Prediction Lengths on LSTM Prediction Performance
3.2. Analysis of Prediction Results at Different Points
3.3. Migration Analysis
3.3.1. Migration Analysis for Monthly Changes
3.3.2. Mobility Analysis of Seasonal Changes
4. Conclusions
- (1)
- The input and prediction lengths will affect the prediction performance of the LSTM model. The increase of the input length can improve the prediction performance of the LSTM model to a certain extent, but no obvious positive correlation is seen between them. Meanwhile, the prediction performance of the LSTM model decreases with the increase of the prediction length, and an obvious negative correlation is seen between them. The effect is the best when the prediction length is 1 and the worst when it is 5.
- (2)
- The prediction results of the LSTM model for a single site are quite accurate, but the extremum cannot be well displayed. Furthermore, affected by the seasonal variation of the Yangtze River Estuary, the prediction result of the Yangtze River Estuary site is the worst compared with other regions.
- (3)
- By analyzing the AE and RMSE of the prediction results of the LSTM model, most of the error is found to be less than 0.4 °C and 0.5 °C, respectively, and the LSTM model has a very successful migration in the East China Sea. In addition, the AE and RMSE of the seasonal and monthly average have prominent spatial characteristics. The places with larger error are distributed in the Yangtze River estuary and its north, the Kuroshio, and the Min-Zhe coastal current.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Location | L1 | L2 | L3 | L4 | |
---|---|---|---|---|---|
Length of Input | |||||
2 | 0.3465 | 0.2698 | 0.1786 | 0.3331 | |
5 | 0.2741 | 0.0568 | 0.0458 | 0.0769 | |
10 | 0.2730 | 0.0917 | 0.0707 | 0.0764 | |
15 | 0.2461 | 0.0995 | 0.1005 | 0.0698 |
Location | L1 | L2 | L3 | L4 | |
---|---|---|---|---|---|
Length of Input | |||||
2 | 0.9976 | 0.9830 | 0.9949 | 0.9884 | |
5 | 0.9985 | 0.9992 | 0.9996 | 0.9993 | |
10 | 0.9985 | 0.9980 | 0.9992 | 0.9994 | |
15 | 0.9988 | 0.9977 | 0.9984 | 0.9995 |
Location | L1 | L2 | L3 | L4 | |||||
---|---|---|---|---|---|---|---|---|---|
Length of Input | Max | Mean | Max | Mean | Max | Mean | Max | Mean | |
2 | 1.3978 | 0.2454 | 0.9512 | 0.1979 | 0.7163 | 0.1356 | 1.1755 | 0.2471 | |
5 | 1.1656 | 0.1968 | 0.2773 | 0.0406 | 0.1893 | 0.0328 | 0.3873 | 0.0574 | |
10 | 1.0081 | 0.2003 | 0.5757 | 0.0634 | 0.2271 | 0.0540 | 0.3401 | 0.0551 | |
15 | 0.8816 | 0.1833 | 0.5338 | 0.0724 | 0.3605 | 0.0773 | 0.3624 | 0.0500 | |
Improve Rate | 36.93% | 25.31% | 70.85% | 79.48% | 73.57% | 75.81% | 71.07% | 79.77% |
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Jia, X.; Ji, Q.; Han, L.; Liu, Y.; Han, G.; Lin, X. Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network. Remote Sens. 2022, 14, 3300. https://doi.org/10.3390/rs14143300
Jia X, Ji Q, Han L, Liu Y, Han G, Lin X. Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network. Remote Sensing. 2022; 14(14):3300. https://doi.org/10.3390/rs14143300
Chicago/Turabian StyleJia, Xiaoyan, Qiyan Ji, Lei Han, Yu Liu, Guoqing Han, and Xiayan Lin. 2022. "Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network" Remote Sensing 14, no. 14: 3300. https://doi.org/10.3390/rs14143300