Prediction of Dominant Ocean Parameters for Sustainable Marine Environment

Prediction of ocean parameters is the rising interest in ocean-related fields to perceive variations in climatic conditions. Most of the existing methods reveal that predictions involve a single parameter, namely Sea Surface Temperature (SST). This paper proposed a deep learning technique of Multi-L...

Full description

Bibliographic Details
Published in:IEEE Access
Main Authors: D. Menaka, Sabitha Gauni
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2021
Subjects:
Deep learning
spatial-temporal prediction
high-speed multilayer convolutional LSTM
internet of underwater things
long and short term memory
weather forecasting
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Arctic
Arctic Ocean
Indian
Online Access:https://doi.org/10.1109/ACCESS.2021.3122237
https://doaj.org/article/bac21c15196d4a0192c7533a328b11b6
Description
Summary:Prediction of ocean parameters is the rising interest in ocean-related fields to perceive variations in climatic conditions. Most of the existing methods reveal that predictions involve a single parameter, namely Sea Surface Temperature (SST). This paper proposed a deep learning technique of Multi-Layer Perceptron (MLP) with Multi-Variant Convolutional (MVC) High Speed (HS) Long and short-Term Memory (HM-LSTM) model to predict the four essential parameters - temperature, pressure, salinity and density at three different Oceans -the Bay of Bengal, Arctic Ocean, and the Indian Ocean. The traditional method is limited to time sequence prediction without considering its spatial linkage. The horizontal and vertical parametric variations with spatial and temporal dependencies at 2000 m below the ocean is the observation considerations for the proposed prediction model. The ARGO provides the thermocline, pycnocline, and halocline layers data to perform the parameter prediction. Its results demonstrate the excellent overall accuracy, low Root Mean Square Error (RMSE), and low Mean Absolute Error (MAE) without any overfitting or underfitting compared to the current State-of-the-art. The forecasting of ocean weather helps conserve the ocean environment for human life in food security, developing the global economy, biomedical exploration, medicines, treatments, diagnostic analysis, and producing a significant passenger transport and tourism source.

Similar Items