An interpretable XGBoost-based approach for Arctic navigation risk assessment.

Yao, Shuaiyu; Wu, Qinhao; Kang, Qi; Chen, Yu-Wang; Lu, Yi

Yao, Shuaiyu; Wu, Qinhao; Kang, Qi; Chen, Yu-Wang; Lu, Yi.

Afiliação

Yao S; Department of Control Science and Engineering, Tongji University, Shanghai, China.
Wu Q; Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
Kang Q; Department of Control Science and Engineering, Tongji University, Shanghai, China.
Chen YW; Alliance Manchester Business School (AMBS), The University of Manchester, Manchester, UK.
Lu Y; COSCO Shipping Special Transportation Co., Ltd, Guangzhou, China.

Risk Anal ; 44(2): 459-476, 2024 Feb.

Article em En | MEDLINE | ID: mdl-37330273

Palavras-chave

Arctic navigation; interpretable machine learning; reproduction of expert judgments; safety risk assessment

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

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