Deep Learning for Imputation and Forecasting Tidal Level

Yang, Cheng‐Hong; Wu, Chih-Hsien; Hsieh, Chih-Min; Wang, Yichuan; Tsen, I-Fan; Tseng, Shih-Huan

doi:10.1109/joe.2021.3073931

Cited by 18 publications

(3 citation statements)

References 47 publications

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“…It can be observed that the NARX-GRU and NARMAX-GRU models with smaller lags, trained with SGDM have better performances than other models. The best NARX-RNN-MTL model reported in Table 2 can be written as (9):…”

Section: Narx and Narmax Combine With Gru Lstm And Bilstmmentioning

confidence: 99%

“…A classical way to make tide level forecasting is by implementing harmonic analysis method. Such a traditional way of forecasting can be ineffective if the data are incomplete (e.g., with some data being missing) [9]. Harmonic analysis methods usually also demand a substantial amount of parameters because such a method needs to use not only astronomical but also non-astronomical features [2], [10].…”

Section: Introductionmentioning

confidence: 99%

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Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Nikentari,

Wei

2024

IJECE

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Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.

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Section: Narx and Narmax Combine With Gru Lstm And Bilstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Nikentari,

Wei

2024

IJECE

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“…Applications of ML models in ocean and coastal engineering have received a great deal of attention recently [17]. This area includes several objectives, such as prediction of wave height [18], prediction of water level and tides [19], breakwater simulation [13], and ocean current simulation [20]. In the realm of HSW modeling, which is the main topic of this study, Mahjoobi and Mosabbeb [21] utilized machine learning models, such as a regressive support vector machine (SVM) model, a multilayer perceptron (MLP) neural network, and a radial basis function (RBF) neural network, to predict short-term HSW based on waves and wind in Lake Michigan.…”

Section: Introductionmentioning

confidence: 99%

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

et al. 2023

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The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage.

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Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques

Chang,

Huang,

Haobijam

et al. 2025

Ecological Informatics

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Deep Learning for Imputation and Forecasting Tidal Level

Cited by 18 publications

References 47 publications

Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques

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