Deep blue AI: A new bridge from data to knowledge for the ocean science

Chen, Ge; Huang, Baoxiang; Chen, Xiaoyan; Ge, Linyao; Radenkovic, Milena; Ma, Ying

doi:10.1016/j.dsr.2022.103886

Cited by 10 publications

(7 citation statements)

References 80 publications

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“…Marine rangeland is an important economic component of fisheries, and the combination of marine science and artificial intelligence provides an important tool for the prediction of water quality parameters [1].…”

Section: Introductionmentioning

confidence: 99%

A Water Quality Prediction Method Based on Deep LSTM Network

Jiang,

Hu,

Huang

et al. 2024

J. Phys.: Conf. Ser.

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Water quality parameters are key factors affecting marine ranching. The water quality parameters are not consistent, which are usually complex and variable. The traditional water quality prediction methods have problems such as low long-term prediction accuracy and weak generalization ability. In order to solve the above problems, this paper proposes a multivariate water quality prediction model based on WT-LSTM, which is compared with the BP neural network model for short-term prediction and long-term prediction of dissolved oxygen water quality parameters, and the experimental results show that the WT-LSTM model has good accuracy and generalization in both short-term and long-term prediction, and the short-term prediction accuracy can be up to 98.47% and the long-term prediction accuracy can be up to 98.28%.

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Section: Introductionmentioning

confidence: 99%

A Water Quality Prediction Method Based on Deep LSTM Network

Jiang,

Hu,

Huang

et al. 2024

J. Phys.: Conf. Ser.

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“…In recent years, with huge requirements in scientific investigation, energy resource exploitation, and national defense in the deep ocean, great progress has been achieved in the field of deep-sea technology [14] , [15] , [16] . Several major national deep-sea equipment have been established, such as submarines, deep-diving submersible vehicles, deepwater oil or gas wells, and so on [17] , [18] , [19] , [20] .…”

Section: Introductionmentioning

confidence: 99%

Cavitation erosion on different metallic materials under high hydrostatic pressure evaluated with the spatially confined sonoluminescence

Liu,

Luo

et al. 2024

Ultrasonics Sonochemistry

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“…A scientific understanding of the ocean is the first step to realizing the sustainable development of the ocean [1]. With the continuous improvement of observation technology, marine science has entered the era of big data, and the combination with artificial intelligence is obviously convenient [2]. Marine ranching is an important part of the fishery economy.…”

Section: Introductionmentioning

confidence: 99%

Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP

Xu,

Lv,

Chen

et al. 2023

Water

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Water quality is an important factor affecting marine pasture farming. Water quality parameters have the characteristics of time series, showing instability and nonlinearity. Previous water quality prediction models are usually based on specific assumptions and model parameters, which may have limitations for complex water environment systems. Therefore, in order to solve the above problems, this paper combines long short-term memory (LSTM) and backpropagation (BP) neural networks to construct an LSTM-BP combined water quality parameter prediction model and uses the root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) to evaluate the model. Experimental results show that the prediction performance of the LSTM-BP model is better than other models. On the RMSE and MAE indicators, the LSTM-BP model is 76.69% and 79.49% lower than other models, respectively. On the NSE index, the LSTM-BP model has improved by 34.13% compared with other models. The LSTM-BP model can effectively reflect time series characteristics and nonlinear mapping capabilities. This research provides a new method and reference for the prediction of water quality parameters in marine ranching and further enables the intelligent and sustainable development of marine ranching.

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Deep blue AI: A new bridge from data to knowledge for the ocean science

Cited by 10 publications

References 80 publications

A Water Quality Prediction Method Based on Deep LSTM Network

A Water Quality Prediction Method Based on Deep LSTM Network

Cavitation erosion on different metallic materials under high hydrostatic pressure evaluated with the spatially confined sonoluminescence

Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP

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