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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Vydáno v:	Global and planetary change Ročník 121; s. 53 - 63
Hlavní autoři:	Imani, Moslem, You, Rey-Jer, Kuo, Chung-Yen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.10.2014 Elsevier
Témata:	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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Shrnutí:	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
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0921-8181 1872-6364
DOI:	10.1016/j.gloplacha.2014.07.002