Ocean of Things: Marine Environment Monitoring using Discriminatory Model

Abstract

It is generating complex Marine Environment monitoring models for the ocean-based on data from Discriminant Analysis. The Ocean of Things (OoT) is adopted in this paper to help interface multiple sensors to predict climate conditions for Aquatic life monitoring. This study, therefore, contemplates five years of climate observations in two oceans. An effort is made to study the temperature, salinity, current of water, how it varies with relation to the ocean, and the reasons for differences in the location parameters. The findings are based on Canonical correlation, Lambda of Wilks, and characterization in general. It is found that the generated model is useful for weather forecasting and aquatic life monitoring.