The CNN-GRU model with frequency analysis module for sea surface temperature prediction

Han, Ying; Sun, Kaiqiang; Yan, Jianing

doi:10.1007/s00500-023-08172-2

Cited by 10 publications

(3 citation statements)

References 25 publications

(14 reference statements)

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“…In the realm of deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) are commonly used for temperature prediction. For example, Han (2023) and colleagues have combined CNNs with Gated Recurrent Unit (GRU) networks to study sea surface temperatures [12], while Choi (2023) et al have utilized LSTMs to predict sea surface temperatures near the Korean Peninsula [13]. These deep learning models have demonstrated good performance and low error in temperature predictions.…”

Section: Research On Temperature Predictionmentioning

confidence: 99%

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

Yang,

2024

FCIS

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In light of the worsening global climate, providing predictive models for surface temperature and energy consumption is crucial for formulating effective climate action strategies. Initially, a Bi-directional Long Short-Term Memory (BiLSTM) network model is established to predict the maximum surface temperatures over the next century, with the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model serving as a benchmark. To assess the risk levels of climate security, the k-means clustering algorithm is utilized to classify the growth rates of carbon dioxide emissions, enabling the construction of a three-tier climate security early warning index. Subsequently, a hybrid classification model based on Support Vector Machine (SVM) and Random Forest (RF) takes the energy consumption growth rates as inputs and the warning indices as outputs to construct a climate security early warning system. The BiLSTM model is employed to predict the energy consumption growth rates for the upcoming decade, and these rates are input into the SVM-RF model to forecast future warning levels. The study demonstrates that the model can effectively predict the maximum surface temperatures and provide a three-tier safety warning system for future climate risk management. The intent of this research is to offer a novel tool for global climate prevention and to deliver practical application value to policymakers in finance, energy, and environmental sectors.

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Section: Research On Temperature Predictionmentioning

confidence: 99%

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

Yang,

2024

FCIS

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“…In 2007, researchers combined harmonics with BP neural networks to forecast tides 18 . Han et al 19 predicted SST by combining CNN and gated recurrent units (GRU) together with frequency analyses, and others used a network model that combines the CNN model with the long short-term memory (LSTM) model in ocean prediction 20 , 21 . A hybrid model can combine the advantages of several models, so the prediction ability is greatly improved.…”

Section: Introductionmentioning

confidence: 99%

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Li,

Zhou,

Wang

et al. 2024

Sci Rep

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Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.

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“…Lastly, AI models could operate independently of seafloor topography data, facilitating seamless model adaptation across diverse regions. In fact, AI technology has been employed in various studies related to many marine phenomena [40][41][42][43][44][45][46][47], including wave height prediction [48][49][50][51][52], chlorophyll concentration forecasting [53], intelligent urban green space detection [54], cloud detection in high-brightness scenes [55], automatic modulation classification [56], and the prediction of MHWs [57].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System

Sun,

Zhou,

Yang

et al. 2023

Remote Sensing

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Marine heatwaves (MHWs) are extreme events characterized by abnormally high sea surface temperatures, and they have significant impacts on marine ecosystems and human society. The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 °C, 0.8 °C, 1.0 °C, and 1.0 °C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings.

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The CNN-GRU model with frequency analysis module for sea surface temperature prediction

Cited by 10 publications

References 25 publications

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System

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