Efficient and explainable ship selection planning in port state control

Abstract

Port state control is the safeguard of maritime transport achieved by inspecting foreign visiting ships and supervising them to rectify the non-compliances detected. One key issue faced by port authorities is to identify ships of higher risk accurately. This study aims to address the ship selection issue by first developing two data-driven ship risk prediction frameworks using features the same as or derived from the current ship selection scheme. Both frameworks are empirically shown to be more efficient than the current ship selection method. Like existing ship risk prediction models, the proposed frameworks are of black-box nature whose working mechanism is opaque. To improve model explainability, local explanation of the prediction of individual ships by the Shapley additive explanations (SHAP) is provided. Furthermore, we innovatively extend the local SHAP model to a near linear-form global surrogate model which is fully-explainable. This demonstrates that the behavior of black-box data-driven models can be as interpretable as white-box models while retaining their prediction accuracy. Numerical experiments demonstrate that the white-box global surrogate models can accurately show the behavior of the original black-box models, shedding light on model validation, fairness verification, and prediction explanation. This study makes the very first attempt in the maritime transport area to quantitatively explain the rationale of black-box prediction models from both local and global perspectives, which facilitates the application of data-driven models and promotes the digital transformation of the traditional shipping industry.