Fusing XGBoost and SHAP Models for Maritime Accident Prediction and Causality Interpretability Analysis

In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pil...

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Bibliographic Details
Published in:	Journal of marine science and engineering Vol. 10; no. 8; p. 1154
Main Authors:	Zhang, Cheng, Zou, Xiong, Lin, Chuan
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2022
Subjects:	Accident prevention Algorithms Casualties causal analysis Climate change Coronaviruses COVID-19 Crew Environmental management extreme gradient boosting algorithm Human error interpretable machine learning Machine learning Marine accidents maritime accidents Maritime safety Navigation Pilotage Ports Prediction models Probability Risk Risk factors risk prediction Safety Ship accidents & safety Shipping Surface navigation Traffic accidents & safety China Zhejiang China
ISSN:	2077-1312, 2077-1312
Online Access:	Get full text
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