Ensemble Learning-Based Impact-Load Severity Appraisal for Offshore Wind Cables Exposed to Submarine Landslide Flows

Offshore wind power cables may experience drag and lift loading where submarine landslides or related density flows cross a cable corridor. To prioritise conditions for detailed analysis, a leakage-controlled ensemble-learning workflow was developed from multi-source submarine landslide–pipeline/cable impact data and evaluated by source-grouped cross-validation. Separately predicted peak drag and peak lift were converted to empirical percentile ranks and averaged to form a dataset-relative peak-load severity index. The resulting Low–Extreme categories achieved accuracy of 0.748, macro F1 of 0.690 and High/Extreme recall of 0.758; binary High/Extreme identification achieved accuracy of 0.903, F1 of 0.800 and ROC-AUC of 0.947. Missingness analyses gave four-category accuracy of 0.717–0.771 across full, low-missingness and complete-case settings, whereas weight and threshold changes showed that category boundaries remain calibration choices rather than universal limits. Scenario and feature-group results indicate combined associations with hydrodynamic forcing, cable geometry, rheology and exposure/cover conditions. The output is intended to rank candidate conditions for computational fluid dynamics, physical modelling or structural verification. It is not a design load, a code check or a full risk estimate, which would additionally require occurrence probability, cable vulnerability, consequence and project-specific validation.