{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:09:11Z","timestamp":1759334951478,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032027276"},{"type":"electronic","value":"9783032027283"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02728-3_12","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T23:59:19Z","timestamp":1759276759000},"page":"140-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transfer Learning Approach for\u00a0Prediction of\u00a0Maximum Wave Height in\u00a0Two Locations of\u00a0the\u00a0Bay of\u00a0Biscay: Bilbao and\u00a0Cabo de Pe\u00f1as"],"prefix":"10.1007","author":[{"given":"Lucia","family":"Porlan-Ferrando","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. David","family":"Nu\u00f1ez-Gonzalez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alain","family":"Ulazia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Gra\u00f1a","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"1","key":"12_CR1","first-page":"83","volume":"8","author":"J Michel","year":"2022","unstructured":"Michel, J., et al.: Deep learning-based significant wave height prediction using wind fields in the Bay of Biscay. Atmos. Sci. Clim. Model. 8(1), 83\u201399 (2022)","journal-title":"Atmos. Sci. Clim. Model."},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/eds.2022.35","volume":"2","author":"M Obakrim","year":"2023","unstructured":"Obakrim, M., et al.: Learning the spatiotemporal relationship between wind and significant wave height using deep learning. Environ. Data Sci. 2, 1\u201315 (2023)","journal-title":"Environ. Data Sci."},{"key":"12_CR3","unstructured":"Kumar, P., et al.: Deep belief networks for sea state prediction and wave load estimation on marine structures. In: Transportation Research Board Annual Meeting, pp. 1\u201310 (2018)"},{"key":"12_CR4","unstructured":"Rodr\u00edguez G\u00e1lvez, J., et al.: Neural network models for rapid tsunami impact prediction along the Spanish coast. In: ECCOMAS Congress 2024 Proceedings, pp. 564\u2013573 (2024)"},{"issue":"3","key":"12_CR5","first-page":"1871","volume":"149","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Zhang, X., Wang, H., Liu, Y., Liu, B.: Deep transfer learning for underwater direction of arrival using one single-vector sensor. J. Acoust. Soc. Am. 149(3), 1871\u20131881 (2021)","journal-title":"J. Acoust. Soc. Am."},{"issue":"15","key":"12_CR6","doi-asserted-by":"publisher","first-page":"4271","DOI":"10.3390\/s20154271","volume":"20","author":"Y Guan","year":"2020","unstructured":"Guan, Y., Pl\u00f6tz, T.: Deep transfer learning for time series data based on sensor modality classification. Sensors 20(15), 4271 (2020)","journal-title":"Sensors"},{"issue":"18","key":"12_CR7","doi-asserted-by":"publisher","first-page":"6791","DOI":"10.3390\/s22186791","volume":"22","author":"Z Zhao","year":"2022","unstructured":"Zhao, Z., Wang, Y., Wang, Z., Zhang, Y.: A sensor fusion method using transfer learning models for machinery fault diagnosis. Sensors 22(18), 6791 (2022)","journal-title":"Sensors"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M., Preciado-Grijalva, A., Wehbe, B.: Pre-trained Models for Sonar Images. arXiv preprint arXiv:2108.01111 (2021)","DOI":"10.23919\/OCEANS44145.2021.9705825"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Knausg\u00e5rd, K.M., Wiklund, A., S\u00f8rdalen, T.K., Halvorsen, K., Kleiven, A.R., Jiao, L., Goodwin, M.: Temperate Fish Detection and Classification: A Deep Learning Based Approach. arXiv preprint arXiv:2005.07518 (2020)","DOI":"10.1007\/s10489-020-02154-9"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Williams, D.P.:. Deep transfer learning across targets and sensors with synthetic aperture sonar data. In: Proceedings of the Institute of Acoustics, vol. 45 (2023)","DOI":"10.25144\/15923"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02728-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T23:59:22Z","timestamp":1759276762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02728-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032027276","9783032027283"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02728-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"A Coru\u00f1a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}