Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

Ti, Zilong; Song, Yubing; Deng, Xiaowei

doi:10.1016/j.apenergy.2022.120027

Cited by 21 publications

(4 citation statements)

References 36 publications

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“…Recurrent neural networks (RNNs) could be helpful in modeling storm behavior and time series of water levels in a sequence prediction framework [43], which requires a longer training time (not dependent on a fixed input size) compared to CNNs. Long short-term memory (LSTM), a subtype of RNN, is a successful model and has been used to capture long-term temporal dependencies of meteorological forcing [64,65] and to analyze the rapid intensification and occurrences of cyclones [66]. A diverse set of base learners (individual learners of the ensemble), such as MLPs, CNNs, and RNNs with appropriate training and tuning, is one empirical way to improve model performance by generating more complex models [67].…”

Section: Neural Network Ensemblementioning

confidence: 99%

“…The focus of this paper is to introduce ensemble methods that can predict storm surge levels using a supervised ANN. Some challenges associated with using ANNs are the inability to capture peak water levels (due to the complex and nonlinear nature of the physical processes) [65,68], long-term processes (which are unavailable due to instrument failures, insufficient data, or sparse observational records), and predictions of storm surges at ungauged sites [43,69]. However, when utilized appropriately, ANN ensemble models have the potential to provide better and faster results than finite element hydrodynamic models.…”

Section: Neural Network Ensemblementioning

confidence: 99%

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Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Nezhad,

Barooni,

Velioglu Sogut

et al. 2023

JMSE

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This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles have shown great potential in improving the accuracy and reliability of such models. This paper presents an overview of the latest research on the application of NNs in storm surge flood modeling and covers the principles and concepts of ENNs, various ensemble architectures, the main challenges associated with NN ensemble algorithms, and their potential benefits in improving flood forecasting accuracy. The main part of this paper pertains to the techniques used to combine a mixed set of predictions from multiple NN models. The combination of these models can lead to improved accuracy, robustness, and generalization performance compared to using a single model. However, generating neural network ensembles also requires careful consideration of the trade-offs between model diversity, model complexity, and computational resources. The ensemble must balance these factors to achieve the best performance. The insights presented in this review paper are particularly relevant for researchers and practitioners working in coastal regions where accurate storm surge flood modeling is critical.

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Section: Neural Network Ensemblementioning

confidence: 99%

Section: Neural Network Ensemblementioning

confidence: 99%

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Nezhad,

Barooni,

Velioglu Sogut

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…This approach broadens the forecasting scope to encompass entire regions. Examples of such comprehensive methodologies include convolutional LSTM networks and multivariate 3-layer LSTMbased methods, which offer a more integrated and regionally encompassing forecast of SWH (Han et al, 2022;Song et al, 2022;Zilong et al, 2022). These sophisticated techniques aspire to capture the complexity of wave dynamics across both time and space for enhanced maritime prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A transformer-based method for correcting significant wave height numerical forecasting errors

Kang,

Song,

Zhang

et al. 2024

Front. Mar. Sci.

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Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During long-term validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings.

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“…To develop a robust predictive model to estimate structural deformation in tunnels, a comprehensive understanding of the problem domain and the requisite inputs and labels is critical [6][7][8]. Constructing an accurate data set is a major challenge, as it often involves extensive effort and substantial resources.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

et al. 2023

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Undercrossing construction can cause severe structural deformation of the above existing tunnel in operation. The induced longitudinal differential settlement between the segments can pose a huge risk to running subways, hence it is of great importance to monitor and predict the settlement. Within this study, a Wireless Sensor Network (WSN) system was implemented to obtain hourly monitoring data of settlement from the very beginning of undercrossing to post construction period. An improved direct multi-step (DMS) forecasting model called ConvRes-DLinear is proposed, which fuses monitoring data with time and process encoding bias to deeply extract and learn temporal correlation of time series. A residual LSTM model is also constructed to compare the accuracy of the improved DLinear model. The training and testing experiment on the monitoring data of longitudinal settlement obtained by WSN system shows that the ConvRes-DLinear model with time and process encoding bias performs surprisingly well with a minimum prediction error. The features of the proposed model are discussed to make the results explainable. The monitoring system and time series forecasting model proposed in this study have a guiding significance for the monitoring and prediction of longitudinal differential settlement of tunnels under environmental disturbance.

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Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

Cited by 21 publications

References 36 publications

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

A transformer-based method for correcting significant wave height numerical forecasting errors

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

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