Nonlinear parameter optimization method for high-resolution monitoring of marine environment

Abstract

In this article, marine environment detection has been studied for improving the high resolution of the environment. The problem of low resolution of marine environment detection is caused by data synthesis defects. The supply chain management (SCM) technology is used to optimize related data to improve the resolution. The main procedure is to first preprocess the obtained hydrological data and eliminate the unreasonable amount represented by extreme values, and then the SCM method was used to estimate the results. Finally, the accuracy of the estimation is evaluated by the cross-validation algorithm. In the example verification, the comparison between the SCM method and the traditional optimal interpolation (OI) method in data integration accuracy has been done. This article compares mean square error, mean absolute error (MAE), root-mean-square error (RMSE), and R 2 parameters. SCM provides better results than OI. Mean error (ME) = 0.6°C/month, MEA = 1.6°C/month, RMSE = 42.3°C/month, and ME and MAE values are lower in summer. It shows that it is sensitive to the lack of data and has a better ability to provide high-resolution and accurate marine environmental data in real time.

Keywords

• marine environmental monitoring
• resolution
• parameter optimization