Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data

Figures & data

Figure 1. Study area map.

Figure 2. Technical roadmap.

Table 1. Parameter optimization results for the three models.

Optuna-Catboost		GA-ANN		GS-RF
Parameters	result	Parameters	results	Parameters	results
Depth	6	learning_rate	0.01	n_estimators	81
(6,10,1)		(0.01,0.03,0.05,0.07,0.09,0.1)		(1,101,10)
border_count	32	hidden_layer	2	max_depth	4
(1,254,1)		(1,5,1)		(4,8,1)
l2_leaf_reg	5	Nodes	5	min_samples_split	3
(5,8,1)		(1,10,1)		(2,5,1)
learning_rate	0.05	Optimizer	Adam	min_samples_leaf	2
(0.01,0.03,0.05,0.07,0.09,0.1)		(SGD、AdaGrad、Adam)		(1,3,1)
Iterations	1,000	Iterations	1,000	max_features	10
(800, 1,200, 100)		(800, 1,200,100)		(1,11,1)

Figure 3. Correlation analysis between SSS and meteorological data.

Figure 4. The relationship between the number of features entered and R²/time.

Figure 5. Significance analysis of meteorological features.

Table 2. Evaluation indicators results of the training set and test set.

models	Indicators
		R^2	RMSE	MAE
training set	Optuna-Catboost	0.9813	0.2147	0.1748
	GA-ANN	0.9490	0.2480	0.2990
	GS-RF	0.9335	0.2685	0.3211
test set	Optuna-Catboost	0.9452	0.2360	0.1816
	GA-ANN	0.9123	0.3004	0.2486
	GS-RF	0.9018	0.3156	0.2641
SMOS L2		0.7424	0.7967	0.5635

Figure 6. Scatter plots of inversion model.

Figure 7. Histogram of the error between the salinity inverted by the model and the salinity measured by Argo.

Figure 8. Spatial distribution of salinity of Argo, Catboost, ANN, RF and SMOS.

Figure 9. Salinity residual distribution of Catboost, ANN, RF and Argo.

Figure 10. R² of the random error model.

Table 3. RMSE and MAE of the random error model.

		2020	1%	2%	5%
Catboost	RMSE/psu	0.2296	0.2310	0.2463	0.2607
	MAE/psu	0.1791	0.1968	0.1824	0.2093
ANN	RMSE/psu	0.2109	0.2065	0.2291	0.2680
	MAE/psu	0.2005	0.2161	0.2183	0.2408
RF	RMSE/psu	0.2044	0.2076	0.2153	0.2203
	MAE/psu	0.1951	0.2071	0.2179	0.2203

Figure 11. Salinity inversion results of machine learning in the south China Sea.

Table 4. The measured buoy site in South China sea.

Number	Longitude(°E)	Latitude(°N)
1	111.927	20.368
2	112.128	19.871
3	113.442	21.554
4	114.419	19.609
5	115.598	20.863
6	116.506	21.082
7	117.040	21.577

Figure 12. Potential relationships between meteorological characteristics and salinity changes.