Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

Sadeghifar, Tayeb; Motlagh, Maryam Nouri; Azad, Masoud Torabi; Mahdizadeh, Mahdi Mohammad

doi:10.1080/01490419.2017.1359220

Cited by 58 publications

(19 citation statements)

References 40 publications

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“…As compared to statistical models, machine learning prediction models provide improved nonlinear trends identifications in time series wave data. ANN, RNN, CNN, and ANFIS based prediction models [102][103][104][105][106] are some of the examples of machine learning models used for wave prediction in the literature. A comparison of time series-based models and physicsbased model (ECMWF) at multiple sites highlights the weakness and strengths of these models [107].…”

Section: Wave Forecastmentioning

confidence: 99%

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Shah

Meng

et al. 2021

Renewable and Sustainable Energy Reviews

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Section: Wave Forecastmentioning

confidence: 99%

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Shah

Meng

et al. 2021

Renewable and Sustainable Energy Reviews

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“…RNNs, which efficiently use temporal information for the sequential analysis of input data, are also suitable for addressing wind-wave prediction problems. Huang et al [50] and Cheng et al [51] have constructed accurate RNN-based wind speed prediction models, and Sadeghifar et al [52], Demetriou et al [53], Zhang et al [54], and Zhou et al [55] have conducted coastal wave height prediction using RNN-based models. In addition, in our previous study, we developed a Two-Step Wind-wave Prediction (TSWP) typhoon wind-wave prediction model, using deep RNNs to forecast wind speed and wave height during typhoons.…”

Section: Related Workmentioning

confidence: 99%

Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

Wei¹

2021

JMSE

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Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.

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“…Mandal et al [25] introduced an artificial neural network RNN with a rprop update algorithm and applied it to SWH forecasting. Sadeghifar et al [26] used RNN to predict the correlation coefficients of SWH at 3 h, 6 h, 12 h, and 24 h to be 0.96, 0.90, 0.87, and 0.73, respectively. Miky et al [27] integrated neural network-based nonlinear autoregressive network and RNN network for SWH prediction.…”

Section: Introductionmentioning

confidence: 99%

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

Hao

et al. 2021

JMSE

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Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.

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Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

Cited by 58 publications

References 40 publications

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

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