DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Gou, Yu; Zhang, Tong; Li, Jun; Li, Wei; Cui, Jun-Hong

doi:10.1109/access.2020.2990939

Cited by 29 publications

(10 citation statements)

References 25 publications

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“…Reference [29] utilized a machine learning approach to the delayed-mode quality control of Argo profiles towards a possible automatic QC system for an Argo data stream. In [30], a deep learning framework was proposed to cope with spatiotemporal ocean sensing data and perform thermocline prediction.…”

Section: Related Workmentioning

confidence: 99%

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

Jiang

Wang

et al. 2021

Security and Communication Networks

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With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.

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Section: Related Workmentioning

confidence: 99%

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

Jiang

Wang

et al. 2021

Security and Communication Networks

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“…Proof-of-concept papers established the important capabilities of self-organizing maps (SOM; e.g., Charantonis et al, 2015;Gueye et al, 2014) and feed-forward or long short-term memory (LSTM) neural networks for hydrographic profile predictions (e.g., Lu, 2019;Jiang et al, 2021;Contractor and Roughan, 2021;Buongiorno Nardelli, 2020;Su et al, 2021;Sammartino et al, 2020). NNs can also efficiently reconstruct Argo interpolated fields (Gou et al, 2020;Meng et al, 2021). A recent study focused on predicting the mixed-layer depth (MLD) from satellites using probabilistic machine learning (Foster et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

et al. 2022

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Abstract. Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T−S) profiles down to 1000 m and the mixed-layer depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T−S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4∘ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The lowest confidence in the prediction is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-Argo years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean stratification, it is also a tool to study how the ocean stratification relates to surface data. We can compute the relative importance of each input for each T−S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results demonstrate the potential of machine learning methods to improve predictions of ocean interior properties from observations of the ocean surface.

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“…• Water temperature and salinity have a direct effect on the acoustic communications between IoUT nodes [266],…”

Section: F ML Techniques In Bmd Applicationsmentioning

confidence: 99%

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Jahanbakht

Wang

Hanzo

et al. 2021

IEEE Commun. Surv. Tutorials

262

111

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The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-theart applications, tools, and techniques. Finally, we analyze of the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Cited by 29 publications

References 25 publications

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

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