# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
###### Abstract  
submarine  
data. [PERSON]'s [6] research on the TSS 350 system, which detects cable routing and burial depth, emphasizes its precision. However, [PERSON]'s system is vulnerable to electromagnetic interference, limiting its effectiveness in environments with high electromagnetic activity. By integrating machine learning models, such interference could potentially be mitigated, allowing for more accurate predictions. [PERSON] et al.'s [7] theoretical method for calculating anchor soil penetration depth offers a mathematical framework for estimating burial depth, though it relies heavily on model testing and numerical simulations. While effective for theoretical calculations, the absence of machine learning techniques to handle large datasets from real-time cable laying processes reduces its practical implementation in dynamic seabed conditions. Other studies, such as [PERSON] et al.'s [8] correlation algorithm for signal extraction, provide enhancements in detection accuracy, surpassing traditional filtering techniques. However, these methods still suffer from environmental noise, a challenge that machine learning algorithms, particularly those integrating data-driven approaches, can potentially resolve. A more recent work by [PERSON] [9] on magnetic field modeling of submarine cables also contributes to the understanding of burial depth by employing curve fitting techniques. However, its reliance on forward modeling limits adaptability to new environments, where data-driven models like the one proposed in this study could be more flexible. [PERSON] et al. [10] derived a formula for determining the minimum burial depth based on factors such as slope height and scour depth in sandy seabed. While useful for sandy seabed, this method's generalizability to other seabed types is limited. A machine learning model, which incorporates diverse environmental and construction data, could improve adaptability across various seabed conditions. Finally, [PERSON] et al.'s [11] method for calculating anchor penetration depth and [PERSON] et al.'s [12] three-dimensional synthetic aperture sonar method for cable measurement, while technologically advanced, still face challenges in adapting to various operational conditions. Machine learning, by contrast, offers the possibility of real-time adaptation to complex environmental variables, making it more suitable for predictive modeling.  
These studies collectively advance the understanding and methodologies for submarine cable protection, highlighting the integration of innovative technologies and practical approaches to enhance the reliability and efficiency of submarine cable installations in complex marine environments.  
The remainder of this paper is organized as follows: Section 2 outlines the data analysis and preprocessing steps, including Z-score normalization and feature encoding techniques. Section 3 explains the methods used in this study, detailing the random forest model and the improved seagull optimization algorithm (ISOA)-backpropagation (BP) neural network model, comparing the performance of the models and discussing the strengths and limitations of the methods. Section 4 provides the conclusions, limitations of the study, and future research directions.