Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

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|Apr 2024

Figures & Tables

Model	RMSE [m]	R²	Δ RMSE [%]	Δ R² [%]	1-R²	Δ 1-R² [%]
Random Forest Regressor	0.035	0.761	51.3	380700.0	0.239	76.1
Extra Trees Regressor	0.035	0.757	50.9	378700.0	0.243	75.7
Extreme Gradient Boosting	0.037	0.739	48.9	369350.0	0.262	73.9
K Neighbors Regressor	0.038	0.725	47.7	362450.0	0.275	72.5
Light Gradient Boosting Machine	0.038	0.715	46.7	357750.0	0.285	71.5
Decision Tree Regressor	0.048	0.546	32.8	273000.0	0.454	54.6
Gradient Boosting Regressor	0.052	0.469	27.3	234600.0	0.531	46.9
Linear Regression	0.069	0.074	3.9	36950.0	0.926	7.4
Least Angle Regression	0.069	0.074	3.9	36950.0	0.926	7.4
Bayesian Ridge	0.069	0.074	3.9	36900.0	0.926	7.4
Ridge Regression	0.069	0.068	3.7	34150.0	0.932	6.8
Huber Regressor	0.070	0.062	3.2	31000.0	0.938	6.2
Orthogonal Matching Pursuit	0.072	0.000	0.2	50.0	1.000	0.0
Lasso Regression	0.072	−0.001	0.2	−150.0	1.001	0.0
Elastic Net	0.072	−0.001	0.2	−150.0	1.001	0.0
Lasso Least Angle Regression	0.072	−0.001	0.2	−150.0	1.001	0.0
Dummy Regressor	0.072	−0.001	0.2	−150.0	1.001	0.0
AdaBoost Regressor	0.073	−0.021	−0.9	−10450.0	1.021	−2.1
Passive Aggressive Regressor	0.086	−0.485	−20.0	−242550.0	1.485	−48.5
sin+cos annual function (baseline)	0.072	0.000	-	-	1.000
sin+cos semiannual function	0.095	0.000	−32.7	0.0	1.000	0.0

DOI: https://doi.org/10.2478/mgrsd-2023-0033 | Journal eISSN: 2084-6118 | Journal ISSN: 0867-6046

Language: English

Page range: 80 - 86

Submitted on: Jan 20, 2024

Accepted on: Apr 21, 2024

Published on: Apr 30, 2024

Published by: Sciendo

In partnership with: Paradigm Publishing Services

Publication frequency: 4 times per year

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© 2024 Viktor Szabó, Katarzyna Osińska-Skotak, Tomasz Olszak, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.