Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Set
2.2. Methodology
2.2.1. Depth and Gravity Anomalies Predictions Using Optimal Machine Learning
2.2.2. GGM Bathymetry Predictions
3. Results and Discussions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Validation | Test | ||||||||
---|---|---|---|---|---|---|---|---|---|
Model | RMSE (m) | R2 | MSE (m2) | MAE (m) | RMSE (m) | R2 | MSE (m2) | MAE (m) | |
Neural Network | Triple Layer Neural Network | 200.09 | 0.96 | 40,037.00 | 141.33 | 232.19 | 0.95 | 53,913.00 | 162.04 |
SVM | Optimization SVM | 384.13 | 0.85 | 147,560.00 | 282.77 | 399.2 | 0.84 | 159,360.00 | 287.23 |
Linear SVM | 506.30 | 0.75 | 256,340.00 | 380.85 | 513.73 | 0.74 | 263,910.00 | 382.00 | |
Quadratic SVM | 436.98 | 0.81 | 190,950.00 | 333.38 | 440.75 | 0.81 | 194,260.00 | 333.84 | |
Cubic SVM | 422.30 | 0.82 | 178,330.00 | 307.75 | 469.16 | 0.78 | 220,120.00 | 326.96 | |
Fine Gaussian SVM | 113.65 | 0.99 | 12,916.00 | 86.44 | 115.28 | 0.99 | 13,289.00 | 86.78 | |
Medium Gaussian SVM | 202.51 | 0.96 | 41,012.00 | 146.08 | 203.03 | 0.96 | 41,219.00 | 145.06 | |
Coarse Gaussian SVM | 338.01 | 0.89 | 114,250.00 | 255.3 | 339.13 | 0.89 | 115,010.00 | 253.56 | |
Decision Tree | Optimization Tree | 49.28 | 1.00 | 2428.20 | 28.65 | 46.42 | 1.00 | 2154.80 | 27.32 |
Fine Tree | 49.32 | 1.00 | 2432.70 | 28.59 | 46.59 | 1.00 | 2170.70 | 27.31 | |
Medium Tree | 56.08 | 1.00 | 3144.80 | 31.55 | 53.54 | 1.00 | 2866.20 | 29.85 | |
Coarse Tree | 77.33 | 0.99 | 5979.50 | 43.68 | 70.91 | 0.99 | 5028.80 | 39.46 | |
GPR | Rational Quadratic GPR | 23.52 | 1.00 | 552.94 | 8.56 | 25.49 | 1.00 | 649.63 | 9.77 |
Squared Exponential GPR | 57.30 | 1.00 | 3283.60 | 33.97 | 73.36 | 0.99 | 5382.10 | 44.43 | |
Matern 5/2 GPR | 30.30 | 1.00 | 918.35 | 14.30 | 32.45 | 1.00 | 1053.20 | 15.46 | |
Exponential GPR | 21.89 | 1.00 | 479.36 | 6.73 | 23.27 | 1.00 | 541.35 | 6.15 | |
Ensemble | Boosting Tree | 210.42 | 0.96 | 44,276.00 | 169.54 | 209.95 | 0.96 | 44,078.00 | 168.86 |
Bagging Tree | 39.06 | 1.00 | 1525.70 | 21.97 | 37.85 | 1.00 | 1432.40 | 21.10 | |
Optimization Tree | 36.10 | 1.00 | 1366.10 | 21.11 | 35.95 | 1.00 | 1292.70 | 20.44 |
Validation | Test | ||||||||
---|---|---|---|---|---|---|---|---|---|
Model | RMSE (mGal) | R2 | MSE (mGal2) | MAE (mGal) | RMSE (mGal) | R2 | MSE (mGal2) | MAE (mGal) | |
Neural Network | Triple Layer Neural Network | 12.53 | 0.66 | 157.06 | 7.11 | 12.57 | 0.68 | 157.96 | 6.92 |
SVM | Optimization SVM | 21.63 | 0.00 | 467.68 | 15.71 | 22.21 | 0.00 | 493.48 | 15.85 |
Linear SVM | 19.78 | 0.16 | 391.35 | 13.63 | 20.47 | 0.15 | 491.10 | 13.79 | |
Quadratic SVM | 19.54 | 0.18 | 381.65 | 12.89 | 20.23 | 0.17 | 609.05 | 13.07 | |
Cubic SVM | 19.14 | 0.22 | 366.19 | 12.55 | 19.71 | 0.21 | 388.34 | 12.65 | |
Fine Gaussian SVM | 10.99 | 0.74 | 120.74 | 3.98 | 11.95 | 0.71 | 142.81 | 4.12 | |
Medium Gaussian SVM | 15.35 | 0.50 | 235.52 | 8.46 | 16.09 | 0.48 | 258.88 | 8.58 | |
Coarse Gaussian SVM | 18.96 | 0.23 | 359.37 | 12.34 | 19.57 | 0.22 | 382.90 | 12.43 | |
Decision Tree | Optimization Tree | 7.35 | 0.88 | 54.00 | 1.92 | 9.03 | 0.83 | 81.52 | 1.98 |
Fine Tree | 7.36 | 0.88 | 54.23 | 1.90 | 9.04 | 0.83 | 81.63 | 1.97 | |
Medium Tree | 7.64 | 0.88 | 58.42 | 2.21 | 8.30 | 0.86 | 68.90 | 2.18 | |
Coarse Tree | 8.65 | 0.84 | 74.79 | 2.95 | 9.15 | 0.83 | 83.70 | 2.86 | |
GPR | Rational Quadratic GPR | 5.90 | 0.93 | 34.75 | 1.37 | 9.03 | 0.83 | 81.51 | 2.58 |
Squared Exponential GPR | 8.64 | 0.84 | 74.71 | 2.67 | 9.30 | 0.82 | 86.48 | 2.84 | |
Matern 5/2 GPR | 7.97 | 0.86 | 63.47 | 2.23 | 7.89 | 0.87 | 62.20 | 2.05 | |
Exponential GPR | 5.00 | 0.95 | 25.03 | 1.12 | 6.52 | 0.91 | 42.50 | 1.60 | |
Ensemble | Boosting Tree | 13.63 | 0.60 | 185.70 | 7.79 | 14.37 | 0.58 | 206.39 | 7.99 |
Bagging Tree | 6.63 | 0.91 | 43.95 | 1.78 | 7.38 | 0.89 | 54.53 | 1.80 | |
Optimization Tree | 6.61 | 0.91 | 43.68 | 1.71 | 7.31 | 0.89 | 53.37 | 1.71 |
DWC Level (m) | −350 | −700 | −1050 | −1400 | −1750 | −2100 | −2450 | −2800 | −3150 | −3500 | −3756 |
---|---|---|---|---|---|---|---|---|---|---|---|
Gravity ratio | 1.26 | 1.71 | 2.20 | 2.76 | 3.38 | 4.09 | 4.94 | 6.03 | 7.57 | 10.09 | 13.24 |
Density contrast (g/cm3) | 1.30 | 1.76 | 2.27 | 2.84 | 3.48 | 4.21 | 5.09 | 6.21 | 7.80 | 10.40 | 13.63 |
Min (m) | Max (m) | Mean (m) | STD (m) | CC (Correlation Coefficient) | ||||
---|---|---|---|---|---|---|---|---|
GGM 1 | GGM 2 | NCEI | ETOPO1 | |||||
GGM 1 | −3719.90 | −52.35 | −2343.56 | 940.43 | 1 | 0.9948 | 0.9962 | 0.9930 |
GGM 2 | −4680.51 | 0.00 | −2359.09 | 959.09 | 0.9948 | 1 | 0.9929 | 0.9911 |
NCEI | −3817.01 | −34.38 | −2341.42 | 950.38 | 0.9962 | 0.9929 | 1 | 0.9957 |
ETOPO1 | −3755.36 | 0.00 | −2348.39 | 951.82 | 0.9930 | 0.9911 | 0.9957 | 1 |
Min (m) | Max (m) | Mean (m) | STD (m) | |
---|---|---|---|---|
GGM 1—GGM 2 | −1023.20 | 2482.61 | 15.53 | 99.08 |
GGM 1—NCEI | −1434.47 | 1340.03 | −2.14 | 82.68 |
GGM 1—ETOPO1 | −1093.55 | 1233.28 | 4.84 | 112.56 |
GGM 2—NCEI | −2825.84 | 1265.74 | −17.67 | 114.23 |
GGM 2—ETOPO1 | −2832.11 | 1124.21 | −10.70 | 127.70 |
NCEI—ETOPO1 | −758.07 | 1045.76 | 6.97 | 88.32 |
Min (m) | Max (m) | Mean (m) | STD (m) | ||
---|---|---|---|---|---|
Profile #1 | GGM 1 | −3719.90 | −1742.00 | −3239.25 | 501.13 |
GGM 2 | −3809.69 | −1807.06 | −3284.22 | 513.44 | |
NCEI | −3720.58 | −1726.38 | −3232.60 | 498.77 | |
ETOPO1 | −3714.17 | −1747.72 | −3221.00 | 485.69 | |
Profile #2 | GGM 1 | −3672.62 | −663.21 | −2381.78 | 914.27 |
GGM 2 | −3819.37 | −691.77 | −2410.15 | 933.96 | |
NCEI | −3671.21 | −626.30 | −2389.71 | 924.21 | |
ETOPO1 | −3755.36 | −655.09 | −2395.70 | 937.36 |
Min (m) | Max (m) | Mean (m) | STD (m) | RMSE (m) | ||
---|---|---|---|---|---|---|
Profile #1 | GGM 1—GGM 2 | −63.02 | 180.69 | 44.97 | 33.64 | 56.10 |
GGM 1—NCEI | −111.86 | 29.68 | −6.65 | 12.69 | 14.29 | |
GGM 1—ETOPO1 | −332.00 | 121.12 | −18.26 | 82.82 | 84.58 | |
GGM 2—NCEI | −208.47 | 73.55 | −51.61 | 38.36 | 64.24 | |
GGM 2—ETOPO1 | −393.22 | 88.34 | −63.22 | 87.08 | 107.41 | |
NCEI—ETOPO1 | −335.87 | 118.84 | −11.61 | 85.06 | 85.62 | |
Profile #2 | GGM 1—GGM 2 | −79.63 | 191.53 | 28.37 | 42.21 | 50.74 |
GGM 1—NCEI | −111.86 | 1135.87 | 7.93 | 102.54 | 102.51 | |
GGM 1—ETOPO1 | −185.84 | 1220.02 | 13.92 | 117.12 | 117.56 | |
GGM 2—NCEI | −162.04 | 955.38 | −20.44 | 94.42 | 96.30 | |
GGM 2—ETOPO1 | −271.37 | 1039.53 | −14.44 | 105.62 | 106.25 | |
NCEI—ETOPO1 | −182.94 | 168.59 | 6.00 | 45.43 | 45.68 |
Min (m) | Max (m) | Mean (m) | STD (m) | RMSE (m) | |
---|---|---|---|---|---|
GGM 1 | −1372.62 | 826.89 | −3.39 | 39.82 | 39.96 |
GGM 2 | −2797.37 | 882.02 | −21.70 | 120.67 | 122.60 |
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Kim, K.B.; Kim, J.; Yun, H.S. Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea. J. Mar. Sci. Eng. 2024, 12, 1520. https://doi.org/10.3390/jmse12091520
Kim KB, Kim J, Yun HS. Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea. Journal of Marine Science and Engineering. 2024; 12(9):1520. https://doi.org/10.3390/jmse12091520
Chicago/Turabian StyleKim, Kwang Bae, Jisung Kim, and Hong Sik Yun. 2024. "Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea" Journal of Marine Science and Engineering 12, no. 9: 1520. https://doi.org/10.3390/jmse12091520