Prediction of sea surface temperatures using deep learning neural networks

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

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“…A hybrid approach has been introduced in [36] that integrates both numerical and data-driven methodologies.…”

Section: Related Workmentioning

confidence: 99%

“…A hybrid approach has been introduced in [ 36 ] that integrates both numerical and data-driven methodologies. This mitigates the drawbacks of just applying the numerical forecast to the sea surface, which exhibits huge variances when applied to a site-specific case study and decreased accuracy for long-term prediction.…”

Section: Related Workmentioning

confidence: 99%

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Ali

Fathalla

Salah

et al. 2021

Computational Intelligence and Neuroscience

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“…Similarly, as anomalies in SST are an important factor in coral outplant survival [210], an algorithm forecasting SST can predict which heat stress may cause a coral bleaching event [211]. Furthermore, it is possible to use deep neural networks to predict SST even more accurately [212]. However, even though the measured SST and the real temperature experienced by reefs can be similar [213], it is not always the case depending on the sensors used and other measurements such as wind, waves and seasons [214].…”

Section: Indirect Sensingmentioning

confidence: 99%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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“…However, these methods lack temporal dependence considerations. Some scholars have used recurrent neural networks (RNNs) and long short-term memory (LSTM) to inverse SST, sea surface height anomaly (SSHA) [41][42][43][44], and so on. However, these have not been fully applied to OHC retrieval, especially for long-term reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives

Qin

Wang

et al. 2021

Remote Sensing

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Global ocean heat content (OHC) is generally estimated using gridded, model and reanalysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term OHC variabilities at decadal to multidecadal scales. In this study, we utilized multisource remote sensing and Argo gridded data based on the long short-term memory (LSTM) neural network method, which considers long temporal dependence to reconstruct a new long time-series OHC dataset (1993–2020) and fill the pre-Argo data gaps. Moreover, we adopted a new machine learning method, i.e., the Light Gradient Boosting Machine (LightGBM), and applied the well-known Random Forests (RFs) method for comparison. The model performance was measured using determination coefficients (R2) and root-mean-square error (RMSE). The results showed that LSTM can effectively improve the OHC prediction accuracy compared with the LightGBM and RFs methods, especially in long-term and deep-sea predictions. The LSTM-estimated result also outperformed the Ocean Projection and Extension neural Network (OPEN) dataset, with an R2 of 0.9590 and an RMSE of 4.45 × 1019 in general in the upper 2000 m for 28 years (1993–2020). The new reconstructed dataset (named OPEN-LSTM) correlated reasonably well with other validated products, showing consistency with similar time-series trends and spatial patterns. The spatiotemporal error distribution between the OPEN-LSTM and IAP datasets was smaller on the global scale, especially in the Atlantic, Southern and Pacific Oceans. The relative error for OPEN-LSTM was the smallest for all ocean basins compared with Argo gridded data. The average global warming trends are 3.26 × 108 J/m2/decade for the pre-Argo (1993–2004) period and 2.67 × 108 J/m2/decade for the time-series (1993–2020) period. This study demonstrates the advantages of LSTM in the time-series reconstruction of OHC, and provides a new dataset for a deeper understanding of ocean and climate events.

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Prediction of sea surface temperatures using deep learning neural networks

Cited by 50 publications

References 17 publications

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives

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