Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks

Li, Wei; Guan, Lei; Qu, Liqin

doi:10.1109/lgrs.2019.2926992

Cited by 53 publications

(30 citation statements)

References 19 publications

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“…vii. SC models should be used to address open questions such as the impact of abrupt changes of SST on coral reefs (Wei et al 2019) and melting ponds and rapid changes in ice thickness in cold regions such as the Arctic and Antarctica (Ressel et al 2015;Ressel and Singha 2016). viii.A closer look at the previous studies has revealed that there is a number of regions where models for SST prediction have not been applied yet and Fig.…”

Section: Discussionmentioning

confidence: 99%

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Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

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The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

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Section: Discussionmentioning

confidence: 99%

“…They found that the LSTM model better reproduces the observed data. In another study, using a similar concept, Wei et al (2019) applied MLP to simulate the SST in the South China Sea, showing accurate predictions.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

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“…This conclusion was further proved by the studies of Wu et al [17]. The ANN-based applications to SST prediction were then conducted in the Arabian Sea, the Australian margin, the western Mediterranean Sea, the Indian Ocean, and the South China Sea [18][19][20][21][22][23][24][25], and researchers tried to predict SST distribution rather than regional mean value in these studies. With the development of deep neural networks through scientific and economic interests, the need has emerged for SST prediction on spatiotemporal sequence scales.…”

Section: Introductionmentioning

confidence: 88%

“…The LSTM, proven to capture the temporal relationship well, was adopted as a neural network model. We used the method mentioned in Wei et al [25] to divide SST into SST anomalies and SST means for separate training. The SST anomaly sequence at each point is correlated with adjacent positions; thus, they were placed in the same model for training and prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

Guan

et al. 2020

Remote Sensing

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Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis（OSTIA）data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

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“…LSTM can overcome the long-term dependencies [15] that occur in the RNN by using a "memory cell" [16]. In LSTM there are three gates namely forget gate, input gate, and output gate [17] which shown in Figure 3. 2) to (7).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Prediction of Sea Surface Current Velocity and Direction Using LSTM

Zulfa

Novitasari

Setiawan³

et al. 2021

Indonesian J. Electron. Instrum. Syst.

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Labuan Bajo is considered to have an important role as a transportation route for traders and tourists. Therefore, it is necessary to have a further understanding of the condition of the waters in Labuan Bajo, one of them is sea currents. The purpose of this research is to predict sea surface flow velocity and direction using LSTM. There are many prediction methods, one of them is Long short-term memory (LSTM). The fundamental of LSTM is to process information from the previous memory by going through three gates, that is forget gate, input gate, and output gate so the output will be the input in the next process. Based on trials with several parameters namely Hidden Layer, Learning Rate, Batch Size, and Learning rate drop period, it achieved the smallest MAPE values of U and V components of 14.15% and 8.43% with 50 hidden layers, 32 Batch size and 150 Learn rate drop.

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Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks

Cited by 53 publications

References 19 publications

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

Prediction of Sea Surface Current Velocity and Direction Using LSTM

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