Application of recurrent neural network for prediction of the time-varying storm surge

Igarashi, Yusuke; Tajima, Yoshimitsu

doi:10.1080/21664250.2020.1868736

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…The 12 processes of storm surge with the meteorological data (air pressure, wind speed, wind direction) and the water level data which were recorded at Lusi tidal station in Jiangsu Province, China from 1986 to 2016 are used to train the hyperparameters of the LSTM model. Although some scholars used neural network to predict the time-varying storm surge which is calculated by subtracting the astronomical tidal level from the total water level [57], the data used in this study is the total water level including the pure astronomical tidal level and the typhoon-induced water level due to that the key criterion usually considered by the marine management departments is the total water level, especially the peak water level prone to exceed the warning limit.…”

Section: Model Results Of Lstmmentioning

confidence: 99%

“…The intensity forecast of TC is usually concentrated on predicting the maximum wind speed or minimum sea level pressure at the center of a TC [54][55][56]. The oceanographic prediction responding to the meteorological action of TC using Neural Networks involves the prediction of storm surge [26,[57][58][59], extreme waves [60][61][62][63][64][65], etc. The morphodynamical prediction responding to the oceanographic action of TC applies Neural Networks to sandbar movement [66][67][68], seasonal beach profile changes [69], and longshore sediment transport [70,71].…”

Section: Introductionmentioning

confidence: 99%

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Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

et al. 2021

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As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.

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Section: Model Results Of Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

et al. 2021

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“…Igarashi et al [37] has also employed a standard recurrent neural network by utilizing a database of about 150 storm datasets to estimate the surge for upcoming storms. Furthermore, Chen et al [38] have applied a standard modification of the time-series model called Long Short-Term Memory (LSTM) model and trained it with a database of twelve storms.…”

Section: Storm Surge Prediction Problem Characteristicsmentioning

confidence: 99%

An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions

Adeli¹,

Sun²,

Wang³

et al. 2022

Preprint

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In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/ intensity history, leveraging a database of synthetic storm simulations. Traditionally, Computational Fluid Dynamics (CFD) solvers are employed to numerically solve the storm surge governing equations that are Partial Differential Equations (PDE) and are generally very costly to simulate. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations. This model can serve as a fast and affordable emulator for the very expensive CFD solvers. The neural network model is trained with the storm track parameters used to drive the CFD solvers, and the output of the model is the time-series evolution of the predicted storm surge across multiple nodes within the spatial domain of interest. Once the model is trained, it can be deployed for further predictions based on new storm track inputs. The developed neural network model is a time-series model, a Long short-term memory (LSTM), a variation of Recurrent Neural Network (RNN), which is enriched with Convolutional Neural Networks (CNNs). The convolutional neural network is employed to capture the correlation of data 1

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“…However, the parametric description of complex systems, such as large-scale, non-frontal, low-pressure tropical cyclones, is intrinsically difficult to determine. As an alternative approach to these models, data-driven methods such as multiple linear regression [26,46], decision tree, ANN [40,42,43,[47][48][49][50], and support vector machine [51,52] have been widely used for the prediction of storm surge heights. In most of studies where data-driven surrogate models are trained with physics-based simulations, such as ADCIRC [37,42,52], a major hurdle is the lack of sufficiently long datasets for training, validating and testing the surrogate models.…”

Section: Introductionmentioning

confidence: 99%

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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Application of recurrent neural network for prediction of the time-varying storm surge

Cited by 19 publications

References 19 publications

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions

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

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