# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 4 Experimental Results and Analysis  
The experimental data of this paper come from two parts, The first part is satellite SST data, which comes from National Oceanic and Atmospheric Administration (NOAA) high-resolution ocean surface temperature data, in which the resolution is 0.25\({}^{\circ}\). SST data include daily data from 1 January 2016 to 31 December 2016. The range is from 22.125\({}^{\circ}\) N to 34.875\({}^{\circ}\) N, 117.125\({}^{\circ}\) E to 121.875\({}^{\circ}\) E, basically covering the East China Sea. The second part is the actual measured data of 55 location points in the East China Sea. The location points are within the range of the first part of the data, and the time is the same as that of the first part of the data. Experiment related parameters: the number of hidden layer units in the first layer is 256, the number of hidden layer units in the second layer is 64, the Adam optimizer is used for learning, the learning rate is 0.0001, and the cross-entropy loss function is used. In the experiment, the window and the step size are 100 and 30, respectively, and the fully connected output layer is 373 (consisting of 373 points), and the probability of each point is output. According to the correlation matrix, the data is classified into two types, and the two types of data are assimilated according to the corresponding remote sensing data. The ultimate goal of all algorithms is to extend the algorithm to new data. If you train the model based on all the data, you will not be able to measure the effect of the model on the new data. In order to solve this problem, the data is usually divided into two parts: training set and test set. Divide the data into 70% training set and 30% test set. The training set refers to the dataset that has been assimilated by remote sensing data and site data, and the test set is the dataset that has not been assimilated by remote sensing data and site data. It can be seen from the Figure 4 below that the \(MAE\) basically no longer changes when the number of iterations reaches 300, which means that the model has basically reached the fit. Figure 5 shows the satellite SST data of the front area before assimilation, and Figure 6 shows the assimilated satellite SST data used by Formula (7). It can be seen that the data before and after the assimilation is basically consistent as a whole, so the stability of the data is well maintained, but there are still slight differences in some areas. In order to verify the effectiveness of our method and the accuracy of the prediction results, this paper uses a comparative experiment method to verify the prediction results before and after assimilation. We use baseline methods for comparison: Gradient Boosting Decision Tree (GBDT) [22], Random Forest (RF) [23], and the Kalman Filter (KF) method. The methods are as follows:  
Figure 4: Relationship between iteration times and loss function. Figure 5: Average temperature in March, April, and May before assimilation. (**a**) Average temperature in March. (**b**) Average temperature in April. (**c**) Average temperature in May. It can be seen from the table that our method is better than the baseline method in an all-around way, and as the number of prediction days increases, the effect of our method is more obvious. In specific analysis, the effect of the RF method in the ocean sea temperature prediction method is relatively general, and the GBDT algorithm has a good effect when predicting one day, but as the number of prediction days increases, the effect becomes worse. In particular, the prediction results in the 14-day forecast, which proved the effectiveness of the assimilation method proposed in this paper. In order to further verify the effectiveness of the idea of this paper, we added another set of experimental comparisons, the experimental data we selected are the same as the above experiments.