A Marine Environment Early Warning Algorithm Based on Marine Data Sampled by Multiple Underwater Gliders

Abstract

This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression (DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube (EWDC) to realize the early warning of a new sampled data file. Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.