Spatiotemporal Prediction of Satellite Altimetry Sea Level Anomalies in the Tropical Pacific Ocean

Imani, Moslem; Chen, Yi-Ching; You, Rey-Jer; Lan, Wen Hau; Kuo, Chung-Yen; Chang, Jung-Chieh; Rateb, Ashraf

doi:10.1109/lgrs.2017.2699668

Cited by 22 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning technique was also used to predict sea level anomaly [14], the researchers focused northern and equatorial Pacific region and apply LSTM, Convolutional Neural Network (CNN) + LSTM (ConvLSTM), Sequence LSTM, Sequence LSTM-P deep learning technique. Other researchers developed and validated two machine learning approach to forecasting sea level anomaly, one of the techniques was Support Vector Regression (SVR) and the other was Autoregressive Integrated Moving Average (ARIMA) [15]. The research area was the Pacific Ocean which is highly vulnerable to sea level rise.…”

Section: Previous Workmentioning

confidence: 99%

Sea Level Anomaly and Earthquake Predictions: Endangered Countries Prognostications

Hassan¹,

Utsha²,

Noman³

2020

IJPCC

View full text Add to dashboard Cite

Natural calamities are often unforeseen and cause massive destruction. It is extremely difficult to predict natural disasters. Existing machine learning techniques are not reliable enough to find the affected countries due to earthquakes and rising sea levels. The aim of this paper is to use predictive analysis to find the countries that will be affected by earthquakes and rising sea levels. Also, the purpose is to see how machine learning techniques perform in terms of sudden calamities like earthquakes or slow calamities like rising sea level. The results was deduced by data analysis, and deep learning techniques like Long-Short Term Memory (LSTM). It was found out that using the approached method in this paper can accurately identify the countries that are going to be affected and predict both earthquake and sea level anomalies accurately. For earthquake, the model was able to capture the happening of earthquake events into a certain quarter of the year with the Root Mean Square Error (RMSE) of 0.504. And for sea level rise, the RMSE was 0.064. It was concluded that Deep learning techniques (e.g.-LSTM) work well with slow changes like sea level anomaly rather than earthquakes. The techniques used in this paper can be upgraded further in the future to find and help more endangered countries to be prepared better against these sudden calamities.

show abstract

Section: Previous Workmentioning

confidence: 99%

Sea Level Anomaly and Earthquake Predictions: Endangered Countries Prognostications

Hassan¹,

Utsha²,

Noman³

2020

IJPCC

View full text Add to dashboard Cite

show abstract

“…The prediction of sea surface temperature is usually solved as a time series problem. Examples include support vector regression (SVR) [28] and multi-layer perceptrons (MLP) [29]. With the development of deep learning, it has evolved from the original RNN to improved networks such as long short-term memory (LSTM) and gated recurrent unit (GRU).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sea Surface Temperature Using U-Net Based Model

Ren,

Wang,

Sun

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model’s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies.

show abstract

“…Fu et al [12] designed a hybrid model that integrates empirical model decomposition, singular spectrum analysis, and LS to forecast longterm sea surface anomalies in the South China Sea. Imani et al [13] combined empirical orthogonal function (EOF) and support vector regression (SVR) to forecast long-term sea level anomalies derived from satellite altimetry in the tropical Pacific Ocean. Cui et al [14] presented a new composite analysis-based random forest (RF) approach for high-precision mid-term prediction of the daily area-averaged SSHA in the South China Sea and the Western North Pacific subtropical region.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting

Zhou,

Ren,

Chen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Sea surface height anomaly (SSHA) plays a pivotal role in ocean dynamics and climate systems. This paper develops a graph-based memory recall recurrent neural network (GMR-Net) to achieve accurate and reliable mid-term spatiotemporal prediction of the SSHA field. The proposed method designs a newly developed long-term memory recall (LMR) cell as the building block of the network, which utilizes the proposed memory store recall (MSR) module to learn and capture the mid-and long-term temporal dependencies of the SSHA field. The MSR module can efficiently recall memories stored in the memory bank across multiple timestamps through the proposed graph representation mechanism even after long periods of disturbance. The mid-term SSHA forecasting is performed with a 30-day ahead, and our proposed GMR-Net model achieves high prediction accuracy in different geographical regions: the Tropical Western Pacific (TWP) and the South China Sea (SCS), yielding an RMSE of 0.026 m and 0.035 m, respectively. Compared with advanced prediction models, our proposed GMR-Net model exhibits high reliability and superior performance in mid-term SSHA forecasting. Moreover, marine phenomena, such as Rossby waves, which can cause dramatic changes in sea surface height, are successfully observed from our forecast data, further verifying the effectiveness of our prediction method.

show abstract

Spatiotemporal Prediction of Satellite Altimetry Sea Level Anomalies in the Tropical Pacific Ocean

Cited by 22 publications

References 21 publications

Sea Level Anomaly and Earthquake Predictions: Endangered Countries Prognostications

Sea Level Anomaly and Earthquake Predictions: Endangered Countries Prognostications

Prediction of Sea Surface Temperature Using U-Net Based Model

Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting

Contact Info

Product

Resources

About