Prediction of Dominant Ocean Parameters for Sustainable Marine Environment

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.

Keywords

• Deep learning
• spatial-temporal prediction
• high-speed multilayer convolutional LSTM
• internet of underwater things
• long and short term memory
• weather forecasting