{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:01:04Z","timestamp":1765486864983,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0015306"],"award-info":[{"award-number":["P0015306"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel\u2019s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model\u2019s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented.<\/jats:p>","DOI":"10.3390\/s23031072","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T05:40:02Z","timestamp":1673934002000},"page":"1072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model"],"prefix":"10.3390","volume":"23","author":[{"given":"Donghyun","family":"Kim","sequence":"first","affiliation":[{"name":"Korea Marine Equipment Research Institute, Busan 49111, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9950-2382","authenticated-orcid":false,"given":"Melia Putri","family":"Handayani","sequence":"additional","affiliation":[{"name":"Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangbong","family":"Lee","sequence":"additional","affiliation":[{"name":"Lab021 Shipping Analytics, Busan 48508, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9380-5470","authenticated-orcid":false,"given":"Jihwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/01441647.2013.806604","article-title":"Atmospheric Emissions from Shipping: The Need for Regulation and Approaches to Compliance","volume":"33","author":"Cullinane","year":"2013","journal-title":"Transp. 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