Application of a novel signal decomposition prediction model in minute sea level prediction

Song, Chao; Chen, Xiaohong; Xia, Wenjun; Ding, Xinjun; Xu, Chuang

doi:10.1016/j.oceaneng.2022.111961

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…Each of the two algorithms was combined with back propagation neural network (BPNN) to construct a hybrid model for predicting SST, and the results showed that CEEMD-BPNN was better for predicting SST (Wu et al, 2019). Moreover, Song et al (2022a) combined three signal decomposition techniques (TVF-EMD, WT and CEEMD) with ENN models for a minute-scale sea level prediction study, respectively, and in the time series lengths of 1,440, 720, and 360 min, the mean R2 values of TVF-EMD-ENN were best among the four models. For the 1,440 min sequence length, the average R2 of TVF-EMD-ENN was 0.952, higher than those of WT-ENN (0.910) and CEEMD-ENN (0.929).…”

Section: Signal Decomposition Hybrid Modelmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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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.

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Section: Signal Decomposition Hybrid Modelmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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“…For this reason, AI methods are chosen in the examination of coastal areas (Guillou and Chapalain, 2021;Karsavran, 2023). Likewise, Song et al (2022) reported that sea water level prediction with neural networks was successfully used in studies. For example, Imani et al (2018) used machine learning to predict water level forecasts in Chiayi Beach.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the performance of SVR and ANN Models in Sea Water Level Prediction for the Izmir Coast of the Aegean Sea

Karsavran

2024

IJONFEST

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Prediction of sea water level is a very important phenomenon for making future projections for flood control, human living conditions and coastal planning. However, it is not easy to estimate sea water level due to the influence of atmospheric conditions. Thus, Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are used for the prediction of seawater level of Izmir Coast based on the time series data. Coefficient of determination (R2 ) and root mean square error (RMSE) are applied as model evaluation criteria in this study. 19 months of seawater level data in the time series were used in this study. The results show that the ANN model can predict the water level with R2 of 0.84 and 0.68 for 1st and 2nd days, respectively, while the SVR model can predict the water level with R2 of 0.83 and 0.69 for 1st and 2nd days, respectively.

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“…Malik et al integrated EMD with neural networks to predict multi-step time series, leveraging the ability of EMD to reduce the volatility of decomposed sequences [6]; however, the IMF components obtained from EMD suffer from mode mixing. Song et al introduced CEEMD decomposition in sea level prediction [7], partially alleviating the mode mixing issue but introducing significant errors. It was not until the introduction of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that this challenge was addressed [8], which inherits the advantages of the aforementioned methods in handling non-linear and non-stationary signal sequences, while possessing adaptive decomposition characteristics [9].…”

Section: Introductionmentioning

confidence: 99%

“…x trend,j − min(X trend ) max(X trend ) − min(X trend ) (7) According to the above information, the summary of extracting historical load trend features can be divided into six steps. Firstly, the historical four-year power load data corresponding to the target sequence time period are added, and the average value is obtained.…”

mentioning

confidence: 99%

Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

Hong

Wang

et al. 2023

Sustainability

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Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.

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Application of a novel signal decomposition prediction model in minute sea level prediction

Cited by 9 publications

References 25 publications

A review of artificial intelligence in marine science

A review of artificial intelligence in marine science

Evaluation of the performance of SVR and ANN Models in Sea Water Level Prediction for the Izmir Coast of the Aegean Sea

Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

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