{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:39:19Z","timestamp":1780990759195,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sea fog is a natural phenomenon that reduces the visibility of manned vehicles and vessels that rely on the visual interpretation of traffic. Fog clearance, also known as fog dissipation, is a relatively under-researched area when compared with fog prediction. In this work, we first analyzed meteorological observations that relate to fog dissipation in Incheon port (one of the most important ports for the South Korean economy) and Haeundae beach (the most populated and famous resort beach near Busan port). Next, we modeled fog dissipation using two separate algorithms, classification and regression, and a model with nine machine learning and three deep learning techniques. In general, the applied methods demonstrated high prediction accuracy, with extra trees and recurrent neural nets performing best in the classification task and feed-forward neural nets in the regression task.<\/jats:p>","DOI":"10.3390\/s21155232","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T08:44:11Z","timestamp":1627893851000},"page":"5232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Sea Fog Dissipation Prediction in Incheon Port and Haeundae Beach Using Machine Learning and Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Jin Hyun","family":"Han","sequence":"first","affiliation":[{"name":"Underwater Survey Technology 21, Incheon 21999, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuk Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Underwater Survey Technology 21, Incheon 21999, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-9896","authenticated-orcid":false,"given":"Hyun Seok","family":"Joo","sequence":"additional","affiliation":[{"name":"Underwater Survey Technology 21, Incheon 21999, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Hyun","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Taeg","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea Hydrographic and Oceanographic Agency, Busan 49111, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seok Jae","family":"Kwon","sequence":"additional","affiliation":[{"name":"Korea Hydrographic and Oceanographic Agency, Busan 49111, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,2]]},"reference":[{"key":"ref_1","first-page":"34","article-title":"Solutions of fog and haze weather from the perspective of economic development","volume":"31","author":"Zheng","year":"2015","journal-title":"Ecol. 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