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...

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Autores principales:	D. Menaka, Sabitha Gauni
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	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
Acceso en línea:	https://doaj.org/article/bac21c15196d4a0192c7533a328b11b6
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id	oai:doaj.org-article:bac21c15196d4a0192c7533a328b11b6
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spelling	oai:doaj.org-article:bac21c15196d4a0192c7533a328b11b62021-11-09T00:03:33ZPrediction of Dominant Ocean Parameters for Sustainable Marine Environment2169-353610.1109/ACCESS.2021.3122237https://doaj.org/article/bac21c15196d4a0192c7533a328b11b62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9584920/https://doaj.org/toc/2169-3536Prediction 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.D. MenakaSabitha GauniIEEEarticleDeep learningspatial-temporal predictionhigh-speed multilayer convolutional LSTMinternet of underwater thingslong and short term memoryweather forecastingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146578-146591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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
spellingShingle	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 D. Menaka Sabitha Gauni Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
description	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.
format	article
author	D. Menaka Sabitha Gauni
author_facet	D. Menaka Sabitha Gauni
author_sort	D. Menaka
title	Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
title_short	Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
title_full	Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
title_fullStr	Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
title_full_unstemmed	Prediction of Dominant Ocean Parameters for Sustainable Marine Environment
title_sort	prediction of dominant ocean parameters for sustainable marine environment
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/bac21c15196d4a0192c7533a328b11b6
work_keys_str_mv	AT dmenaka predictionofdominantoceanparametersforsustainablemarineenvironment AT sabithagauni predictionofdominantoceanparametersforsustainablemarineenvironment
_version_	1718441418878353408

Prediction of Dominant Ocean Parameters for Sustainable Marine Environment

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