Abstract
Researchers detail new data in artific ial intelligence. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction da ta." Our news editors obtained a quote from the research from Shanghai Ocean Universi ty: "To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict th e weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature co rrelations. The random forest method is then used to determine the weights of fa ctors, 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 re gression, decision tree, gradient decision tree, and the BP network before optim ization. The results show that the random forest method effectively quantifies t he impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors."
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