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As global warming accelerates, melting Arctic ice caps are likely to increase traffic in the NSR and enhance its commercial viability. Due to the harsh Arctic environment imposing threats to the safety of ship navigation, it is necessary to assess Arctic navigation risk to maintain shipping safety. Currently, most studies are focused on the conventional assessment of the risk, which lacks the validation based on actual data. In this study, actual data about Arctic navigation environment and related expert judgments were used to generate a structured data set. Based on the structured data set, extreme gradient boosting (XGBoost) and alternative methods were used to establish models for the assessment of Arctic navigation risk, which were validated using cross\u2010validation. The results show that compared with alternative models, XGBoost models have the best performance in terms of mean absolute errors and root mean squared errors. The XGBoost models can learn and reproduce expert judgments and knowledge for the assessment of Arctic navigation risk. Feature importance (FI) and shapley additive explanations (SHAP) are used to further interpret the relationship between input data and predictions. The application of XGBoost, FI, and SHAP is aimed to improve the safety of Arctic shipping using advanced artificial intelligence techniques. The validated assessment enhances the quality and robustness of assessment.<\/jats:p>","DOI":"10.1111\/risa.14175","type":"journal-article","created":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T01:48:37Z","timestamp":1687052917000},"page":"459-476","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An interpretable XGBoost\u2010based approach for Arctic navigation risk assessment"],"prefix":"10.1111","volume":"44","author":[{"given":"Shuaiyu","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2847-8394","authenticated-orcid":false,"given":"Qinhao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu\u2010Wang","family":"Chen","sequence":"additional","affiliation":[{"name":"Alliance Manchester Business School (AMBS) The University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Lu","sequence":"additional","affiliation":[{"name":"COSCO Shipping Special Transportation Co., Ltd  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2017.02.002"},{"issue":"2","key":"e_1_2_7_3_1","first-page":"255","article-title":"KEEL data\u2010mining software tool: Data set repository, integration of algorithms and experimental analysis framework","volume":"17","author":"Alcal\u00e1\u2010Fdez J.","year":"2011","journal-title":"Journal of Multiple\u2010Valued Logic and Soft Computing"},{"key":"e_1_2_7_4_1","volume-title":"Safety and shipping review 2021","author":"Allianz","year":"2021"},{"key":"e_1_2_7_5_1","doi-asserted-by":"crossref","unstructured":"Andrews D. 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