Kuang, Y.; Yuan, J., and Chen, Z., 2019. Prediction method of manganese tuberculosis resources content in deep seafloor based on multiple regression. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 90–96. Coconut Creek (Florida), ISSN 0749-0208.

In order to improve the prediction accuracy of deep seafloor manganese nodule resource content, a prediction method of deep seafloor manganese nodule resource content based on multiple regression analysis is proposed. The big data model for statistical analysis of deep seafloor manganese nodules is constructed. The deep seafloor manganese nodules are extracted by mining and feature extraction methods. Combined with the artificial intelligence optimization method, the characteristics of the deep seafloor manganese nodules are classified and studied, and the information fusion is carried out according to the extraction values of the characteristic parameters of the deep seafloor manganese nodules. A statistical analysis and regression analysis model for predicting the content of manganese nodules in deep seafloor is established, and the multivariate linear fitting and recurrent analysis methods are used to predict the content of manganese nodules in deep seafloor. The simulation results show that the method has high accuracy in predicting the content of manganese nodules in the deep seafloor, and improves the statistical analysis ability of the manganese nodules in the deep seafloor.