Prediction of Marine Pycnocline Based on Kernel Support Vector Machine and Convex Optimization Technology

Abstract

With the explosive growth of ocean data, it is of great significance to use ocean observation data to analyze ocean pycnocline data in military field. However, due to natural factors, most of the time the ocean hydrological data is not complete. In this case, predicting the ocean hydrological data by partial data has become a hot spot in marine science. In this paper, based on the traditional statistical analysis literature, we propose a machine-learning ocean hydrological data processing process under big data. At the same time, based on the traditional pycnocline gradient determination method, the open Argo data set is analyzed, and the local characteristics of pycnocline are verified from several aspects combined with the current research about pycnocline. Most importantly, in this paper, the combination of kernel function and support vector machine(SVM) is extended to nonlinear learning by using the idea of machine learning and convex optimization technology. Based on this, the known pycnocline training set is trained, and an accurate model is obtained to predict the pycnocline in unknown domains. In the specific steps, this paper combines the classification problem with the regression problem, and determines the proportion of training set and test formula set by polynomial regression. Subsequently, the feature scaling of the input data accelerated the gradient convergence, and a grid search algorithm with variable step size was proposed to determine the super parameter c and gamma of the SVM model. The prediction results not only used the confusion matrix to analyze the accuracy of GridSearch-SVM with variable step size, but also compared the traditional SVM and the similar algorithm. At the end of the experiment, two features which have the greatest influence on the Marine density thermocline are found out by the feature ranking algorithm based on learning.

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

“…The SVR modelling accuracy can be improved with the right choice of kernel function as different kernel functions have different mapping capabilities. The four kernel functions given in Equations (16) to (19) are most commonly used in the SVR algorithm [ 22 , 35 , 36 , 37 , 38 , 39 , 41 ]: where is the transpose of , r is a constant term, is the polynomial order, and is a RBF kernel parameter that controls the spread of the data while transforming to higher dimensions.…”

“…The SVR modelling accuracy can be improved with the right choice of kernel function as different kernel functions have different mapping capabilities. The four kernel functions given in Equations ( 16) to (19) are most commonly used in the SVR algorithm [22,[35][36][37][38][39]41]:…”