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

Abstract

A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented.

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

“…Feature Importance using Tree-Based Models: This method involves using decision treebased models such as Random Forest, XGBoost, or Gradient Boosting, to determine the importance of each feature in the model. The importance of features is measured based on their contribution to reducing impurity or error in the model (Liu & Aldrich, 2023;Kim et al, 2023;Awotunde et al, 2023).…”

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

“…Only limited studies in the maritime domain have ever leveraged the machine learning implementation with XAI for some cases. Kim et al (2023) developed a machine learning prediction model to estimate the vessel shaft power, then leveraged the prediction model with XAI using SHAP to obtain its feature attribution [37]. In other research, Kim et al (2021) conducted vessel main engine anomaly detection and explained the prediction of whether an instance is considered an anomaly or not using SHAP [38].…”

“…Adapted from [37], Figure 3 shows the individual prediction outcome of f(x) = 10 can be broken down by incorporating the combined contribution value (which is the sum of Shapley values) from all the features, resulting in a value of 1.6 + 0.7 − 2.9 − 0.9 = −1.5. This is then added to the model's fixed base value of 11.5.…”