Long Short-Term Memory approach for Wave Height Prediction: Study Case in Jakarta Bay, Indonesia

2021 ◽  
Author(s):  
Agnesia Peronika Lumban Raja ◽  
Annas Wahyu Ramadhan ◽  
Didit Adytia ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Wave Height ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Height Prediction ◽  
Study Case ◽  
Long Short Term Memory

scholarly journals Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

2021 ◽  
Vol 9 (7) ◽  
pp. 744
Author(s):  
Shuyi Zhou ◽  
Brandon J. Bethel ◽  
Wenjin Sun ◽  
Yang Zhao ◽  
Wenhong Xie ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Wave Height ◽  
Significant Wave Height ◽  
Short Term Memory ◽  
Ocean Engineering ◽  
Short Term ◽  
Term Memory ◽  
Significant Wave ◽  
Mode Decomposition ◽  
Long Short Term Memory

Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

scholarly journals Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

2021 ◽  
Vol 9 (5) ◽  
pp. 514
Author(s):  
Xiaoyu Zhang ◽  
Yongqing Li ◽  
Song Gao ◽  
Peng Ren
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Short Term Memory ◽  
Gaussian Approximation ◽  
Numerical Prediction ◽  
Short Term ◽  
Term Memory ◽  
Numerical Predictions ◽  
Probabilistic Distribution ◽  
Long Short Term Memory

This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

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