Abstract
With the rapid development of sensor networks, big marine data arises. To efficiently use these data to predict thermoclines, we propose a machine learning approach. We firstly focus on analyzing how temperature, salinity, and geographic location features affect the formation of thermocline. Then, an improved model based on entropy value method for the thermocline selection is demonstrated. The experiments adopt BOA Argo data sets and the experimental results show that our novel model can predict thermoclines and related data effectively.
Highlights
The marine environment is important to human living and the exploration of the marine environment has become necessary
Zhang et al demonstrated the distribution characteristics of thermoclines and rules of its variety via the mass observation method [1]. They analyzed the ocean by investigating data from 1900 to 2004 of the South China Sea and the sea area around Taiwan, and got the distribution of thermoclines in coastal areas in China
From the previous we can conclude that the thermoclines are distributed in the region of 100–300 m in depth, Figure 12 experiments we can conclude that the thermoclines are distributed in the region of 100–300 m in shows good-quality samples distributed between 100 m and 150 m in depth
Summary
The marine environment is important to human living and the exploration of the marine environment has become necessary. In order to better study the thermocline and get access to its properties and application scenarios, we use an underwater sensor network to perceive the marine environment. Traditional sensor networks are limited to collect temperature, humidity, As the underwater environment is more complicated than expected, current sensor networks position, intensity, pressure, biochemical and other scalar data; in reality we need more media may not fully meet the needs. Theand basismonth, of the and traditional method, add more characteristics to ensure thermocline-defining betweenyear the and traditional selection method the new including the information onconsistency geography, salinity, month, and get a newand sample method, we set upIna order one-to-one mapping relationship from strength to score. With mapping strength being critical value to score criteria, we could analyze thermoclines The applicability of this method has been proved by the regional (79.5 S, 79.5 N, 180◦ W–80◦ E). We conclude this work and point out an employment prospect
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
