Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model.

Kim, Donghyun; Handayani, Melia Putri; Lee, Sangbong; Lee, Jihwan

Kim, Donghyun; Handayani, Melia Putri; Lee, Sangbong; Lee, Jihwan.

Afiliação

Kim D; Korea Marine Equipment Research Institute, Busan 49111, Republic of Korea.
Handayani MP; Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Lee S; Lab021 Shipping Analytics, Busan 48508, Republic of Korea.
Lee J; Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Sensors (Basel) ; 23(3)2023 Jan 17.

Article em En | MEDLINE | ID: mdl-36772108

Palavras-chave

Shapley Additive exPlanations; TreeExplainer; explainable artificial intelligence; machine learning; maritime engineering; noon report; oceanographic; regression; vessel shaft power

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article