Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming

Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two...

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Veröffentlicht in:Global and planetary change Jg. 121; S. 53 - 63
Hauptverfasser: Imani, Moslem, You, Rey-Jer, Kuo, Chung-Yen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.10.2014
Elsevier
Schlagworte:
Artificial intelligence
Caspian Sea
Caspian Sea level
Earth, ocean, space
Evolutionary
Exact sciences and technology
External geophysics
Gene expression
gene expression programming
Mathematical analysis
Mathematical models
neural networks
Physics of the oceans
prediction
Programming
satellite altimetry
satellites
sea level
support vector machine
Support vector machines
surface water
Surface waves, tides and sea level. Seiches
water supply
Caspian Sea
Eurasia, Caspian Sea
gene expression programming
support vector machine
Caspian Sea level
satellite altimetry
Evolutionary algorithm
Space remote sensing
neural networks
Satellite observation
Sea level rise
Support vector machine
Forecast model
Observation data
sea level
altimetry
ISSN:0921-8181, 1872-6364
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Zusammenfassung:Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE=0.035) and maximum coefficient of determination (R2=0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN. •Caspian Sea level changes are predicted using artificial intelligent approaches.•Using promising SVM and GEP approaches as satisfactory forecasting models•Using time series obtained by satellite altimetry as available high-quality data
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ISSN:0921-8181
1872-6364
DOI:10.1016/j.gloplacha.2014.07.002