Research on Ocean Adaptation Zone Classification Prediction Model Based on Gravity Anomaly data

This paper aims to propel the development of the marine economy by proposing a crucial underwater navigation adaptation zone classification prediction technique. Firstly, through meticulous processing of gravity anomaly benchmark data, the maritime area is delineated, and adaptability is calibrated to ensure the navigation precision of underwater vehicles. Using the K-means classification model, the data is refined and categorized into zones of medium-high accuracy, low accuracy, medium accuracy, and high accuracy. Subsequently, specific feature attribute indicators are selected, and an effective regional adaptation zone classification prediction model system, denoted as F, is established to ensure navigation accuracy within regions with different gravity anomaly feature distributions. Finally, transferability predictions are performed on new gravity anomaly data to explore the model's applicability to novel data, validating the robustness and generalization capability of the model and fostering further advancements in underwater navigation technology. This research holds significant importance for driving the marine economy forward.