Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method
| العنوان: | Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method |
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| المؤلفون: | Ömer Faruk Ertuğrul, Mehmet Emin Tagluk |
| المساهمون: | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümü, Ertuğrul, Ömer Faruk |
| المصدر: | Arabian Journal for Science and Engineering. 42:3289-3298 |
| بيانات النشر: | Springer Science and Business Media LLC, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Multidisciplinary, 010504 meteorology & atmospheric sciences, Artificial neural network, Computer science, business.industry, Global warming, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, PSMSL Database, Behavioral learning, Set (abstract data type), 0202 electrical engineering, electronic engineering, information engineering, Mean Sea Level, 020201 artificial intelligence & image processing, Geophysical Phenomena, Artificial intelligence, Extreme Learning Machine, business, computer, Sea level, Generalized Behavioral Learning Method, 0105 earth and related environmental sciences, Extreme learning machine |
| الوصف: | Determining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL. |
| وصف الملف: | application/pdf |
| تدمد: | 2191-4281 2193-567X |
| DOI: | 10.1007/s13369-017-2468-4 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c460e111f1240274e57eda89180d5ba https://doi.org/10.1007/s13369-017-2468-4 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....3c460e111f1240274e57eda89180d5ba |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21914281 2193567X |
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| DOI: | 10.1007/s13369-017-2468-4 |