Prediction of Dominant Ocean Parameters for Sustainable Marine Environment

D Menaka,Sabitha Gauni

doi:10.1109/access.2021.3122237

Abstract

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.

Highlights

N EARLY 72 percent of the surface of the planet is surrounded by oceans, which provide 97 percent of water to the earth Earth and roughly 70 percent of the oxygen we breathe [1]
The parameters at each algorithm are taken for comparison, which works as an optimal model to evaluate the test set.The detailed flowchart for the working of HM-LSTM is explained in figure 4
The results prove that HM-LSTMs R2score is better than other algorithms with an average of 98% accuracy for the BOB, 95% accuracy for the Indian Ocean, and 97% accuracy for the Arctic Ocean

Summary

INTRODUCTION

N EARLY 72 percent of the surface of the planet is surrounded by oceans, which provide 97 percent of water to the earth Earth and roughly 70 percent of the oxygen we breathe [1]. To preserve the Ocean resources, this paper proposes a multiple-layer convolutional High speed-LSTM (HMLSTM) for forecasting the future values of temperature, pressure, salinity, and density in three different oceans like Bay of Bengal, Indian Ocean, and the Arctic Ocean to observe these parametric variations below 2000m in both horizontal and vertical directions. The experimental results show that the HM-LSTM model performs efficient time series prediction tasks for a large dataset at different oceanic conditions in various ocean parameters. The computational time in HM-LSTM is reduced by training the model with only 50 iterations, and it can be increased only in case of high prediction error This is achieved by the early stopping method by solving the problem of overfitting and underfitting. The parameters at each algorithm are taken for comparison, which works as an optimal model to evaluate the test set.The detailed flowchart for the working of HM-LSTM is explained in figure 4

DATASET

EVALUATION METRICS

Findings

CONCLUSION