Prediction of Sea Surface Current Velocity and Direction Using LSTM

Zulfa, Irkhana Indaka; Novitasari, Dian Candra Rini; Setiawan, Fajar; Fanani, Aris; Hafiyusholeh, Moh.

doi:10.22146/ijeis.63669

Cited by 8 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SWAN-LSTM models integrating near-coastal wave models have also emerged to achieve near-coastal wave height predictions (Fan et al, 2020). As well as a series of experiments with LSTM on currents, sea level, and even ENSO phenomenon (Broni-Bedaiko et al, 2019;Ishida et al, 2020;Zulfa et al, 2021). LSTM models were also shown to be effective in predicting chlorophyll-a concentrations (Cho and Park, 2019;Rostam et al, 2021;Cen et al, 2022).…”

Section: Rnnmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

show abstract

Section: Rnnmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…Evaluation of forecasting results using the root mean square error (RMSE). The purpose of the evaluation is to verify the effectiveness and display the magnitude of the error generated by a prediction model [24]. RMSE is used to detect irregularities or outliers in the prediction system built [25].…”

Section: Modelmentioning

confidence: 99%

LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting

et al. 2022

View full text Add to dashboard Cite

Forecasting is the process of predicting something in the future based on previous patterns. Forecasting will never be 100% accurate because the future has a problem of uncertainty. However, using the right method can make forecasting have a low error rate value to provide a good forecast for the future. This study aims to determine the effect of increasing the number of hidden layers and neurons on the performance of the long short-term memory (LSTM) forecasting method. LSTM performance measurement is done by root mean square error (RMSE) in various architectural scenarios. The LSTM algorithm is considered capable of handling long-term dependencies on its input and can predict data for a relatively long time. Based on research conducted from all models, the best results were obtained with an RMSE value of 0.699 obtained in model 1 with the number of hidden layers 2 and 64 neurons. Adding the number of hidden layers can significantly affect the RMSE results using neurons 16 and 32 in Model 1.

show abstract

“…One approach is the use of deep learning methods such as ConvLSTMP3, which extracts spatial-temporal features of sea surface heights using convolutional operations and long short-term memory (LSTM) (Song et al, 2021). One more paper (Zulfa et al, 2021) Percentage Error (MAPE) values in Labuan Bajo waters. In the paper (Ning et al, 2021) an optimized Simple Recurrent Unit (SRU) deep network was develped for short-to-medium-term sea surface height prediction with AVISO data.…”

Section: Introductionmentioning

confidence: 99%

Advancing Ocean Forecasting in the Russian Arctic: A Performance Analysis of MariNet Model in Comparision to FourCastNet and PhyDNet

Buinyi,

Irishev,

Nikulin

et al. 2024

Preprint

View full text Add to dashboard Cite

Marine forecasts are essential for safe navigation, efficient offshore operations, coastal management, and research, especially in areas with a such harsh conditions as the Arctic Ocean. They require accurate predictions of ocean currents, wind-driven waves, and other oceanic parameters. However, physics-based numerical models, while precise, are computationally demanding. Consequently, data-driven methods, which are less resource-intensive, may offer a more efficient solution for sea state forecasting. This paper presents an analysis and comparison of three data-driven models: our newly developed convLSTM-based MariNet, FourCastNet and the PhydNet, a physics-informed model for video prediction. Using metrics such as RMSE, Bias and Correlation, we demonstrate the areas where our model surpasses the performance of the prominent prediction models. Our model achieves improved accuracy in forecasting ocean dynamics compared to FourCastNet and PhyDNet. We also find that our model requires significantly less training data, computing power, and consequently provides less carbon emmisions. The results suggest that data-driven models should be further explored as a complement to physics-based models for operational marine forecasting. They have the potential to enhance prediction accuracy and efficiency, enabling more responsive and cost-effective forecasting systems.

show abstract

Prediction of Sea Surface Current Velocity and Direction Using LSTM

Cited by 8 publications

References 24 publications

A review of artificial intelligence in marine science

A review of artificial intelligence in marine science

LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting

Advancing Ocean Forecasting in the Russian Arctic: A Performance Analysis of MariNet Model in Comparision to FourCastNet and PhyDNet

Contact Info

Product

Resources

About