Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model’s performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.