{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T21:37:49Z","timestamp":1777325869572,"version":"3.51.4"},"reference-count":25,"publisher":"EDP Sciences","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"vor","delay-in-days":160,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["E3S Web Conf."],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:p>For an efficient wave energy extraction, the evolution of some specific parameters must be known. These parameters, like significant wave height and period, are mainly determined by the wind speed and influenced by some sea environment characteristics. Their evolution in time is one of the basic information necessary for designing of an accurate energy conversion system. In many scientific works the benefits of artificial neural networks based modeling are presented. These models allow the prediction and optimization of the wave parameters starting from experimentally acquired data. Due to specific calculus method of the artificial neural networks, in order to obtain accurate results, a very important step is the appropriate neural model design. If the model is optimal correlated with the data processed, the results obtained can be more significant than those coming from the mathematical formulas. The main neural models parameters that must be taken into account for an optimal design are model structure, transfer function and training algorithm. This paper presents an investigation of the results obtained with different models, proving that for a specific dataset a specific neural model offers the best results. Several models are analyzed, for a dataset corresponding to specific point in Black Sea and a comparison of results is presented.<\/jats:p>","DOI":"10.1051\/e3sconf\/202017303007","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T08:02:40Z","timestamp":1591689760000},"page":"03007","source":"Crossref","is-referenced-by-count":4,"title":["Optimization of Artificial Neural Networks Based Models for Wave Height Prediction"],"prefix":"10.1051","volume":"173","author":[{"given":"Gheorghe","family":"St\u0103v\u0103rache","sequence":"first","affiliation":[]},{"given":"Sorin","family":"Ciortan","sequence":"additional","affiliation":[]},{"given":"Eugen","family":"Rusu","sequence":"additional","affiliation":[]}],"member":"250","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"R1","unstructured":"Falnes J.., Marine Structures, 20, (2004)"},{"key":"R2","doi-asserted-by":"crossref","unstructured":"Krishna-kumar N., Savitha R., Mamun A.A., Regional Ocean Wave Height Prediction using Sequential Learning Neural Networks (2017)","DOI":"10.1016\/j.oceaneng.2016.10.033"},{"issue":"10","key":"R3","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/j.oceaneng.2005.08.007","volume":"33","author":"Mandal","year":"2006","journal-title":"Ocean Engineering"},{"issue":"5","key":"R4","first-page":"9649","volume":"6","author":"Akhil","year":"2017","journal-title":"International Journal of Innovative Research in Science, Engineering and Technology"},{"key":"R5","doi-asserted-by":"crossref","unstructured":"Rojas R., Neural Networks A Systematic Introduction, (1996)","DOI":"10.1007\/978-3-642-61068-4"},{"key":"R6","unstructured":"Krose B., van de Smagt P., An Introduction to Neural Networks, (1996)"},{"key":"R7","unstructured":"Hristev R.M., The ANN Book, (1998)"},{"key":"R8","unstructured":"Alavala C.R., Fuzzy Logic and Neural Networks: Basic Concepts & Applications, (2007)"},{"key":"R9","unstructured":"Blackledge J., Coyle E., Kearney D., McGuirk R., Norton B. Estimation of Wave Energy from Wind Velocity, (2013)"},{"key":"R10","unstructured":"World Meteorological Organization, Guide to Wave Analysis and Forecasting, (1998)"},{"key":"R11","unstructured":"Bretschneider C.L., Generation of Waves by Wind: State of Art, (1965)"},{"key":"R12","unstructured":"Tucker M.J., Pitt E.G., Waves in Ocean Engineering, (2001)"},{"key":"R13","doi-asserted-by":"crossref","unstructured":"Andreas E.L., Wang S., Predicting Significant Wave Height off the Northeast Coast of The United States, (2007)","DOI":"10.1016\/j.oceaneng.2006.08.004"},{"issue":"5","key":"R14","first-page":"604","volume":"8","author":"Sugianto","year":"2017","journal-title":"International Journal of Civil Engineering and Technology"},{"key":"R15","unstructured":"Barth S., Eecen P.J., Description of the Relation of Wind, Wave and Current Characteristics at the Offshore Farm Egmond aan Zee (OWEZ) Location, (2006)"},{"issue":"4","key":"R16","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1260\/1759-3131.5.4.223","volume":"5","author":"Vimala","year":"2014","journal-title":"International Journal of Ocean and Climate System"},{"issue":"2","key":"R17","first-page":"33","volume":"2","author":"Kashikar","year":"2014","journal-title":"International Journal of soft Computing and Artificial Inteligence"},{"issue":"1","key":"R18","first-page":"82","volume":"43","author":"Vimala","year":"2014","journal-title":"Indian Journal of Geo-Marine Sciences"},{"key":"R19","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.cageo.2004.10.005","volume":"31","author":"Makarynkyy","year":"2005","journal-title":"Computer & Geosciences"},{"issue":"7","key":"R20","first-page":"744","volume":"3","author":"Kashikar","year":"2014","journal-title":"International Journal of Engineering Research & Technology"},{"key":"R21","unstructured":"Hagan M.T., Demuth H.B., Beale M.H., Jesus O.D., Neural Network Design 2nd ed., (2014)"},{"issue":"2","key":"R22","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1177\/1759313116642896","volume":"7","author":"Gopinath","year":"2016","journal-title":"International Journal of Ocean and Climate System"},{"issue":"1","key":"R23","first-page":"82","volume":"43","author":"Vimala","year":"2014","journal-title":"International Journal of Geo-Marine Sciences"},{"issue":"5","key":"R24","first-page":"10","volume":"4","author":"Shrivastava","year":"2018","journal-title":"Smart Moves IJOScience"},{"key":"R25","unstructured":"Makarynkyy O., Makarynska D., Rusu E., Filling Gaps in Wave Records with Artificial Neural Networks, (2006)"}],"container-title":["E3S Web of Conferences"],"original-title":[],"link":[{"URL":"https:\/\/www.e3s-conferences.org\/10.1051\/e3sconf\/202017303007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T08:05:25Z","timestamp":1591689925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.e3s-conferences.org\/10.1051\/e3sconf\/202017303007"}},"subtitle":[],"editor":[{"given":"M.L.","family":"Kolhe","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":25,"alternative-id":["e3sconf_icacer2020_03007"],"URL":"https:\/\/doi.org\/10.1051\/e3sconf\/202017303007","relation":{},"ISSN":["2267-1242"],"issn-type":[{"value":"2267-1242","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}