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  1. Improving wave height prediction accuracy with deep …

    A novel convolutional neural network-long short-term memory (CNN-LSTM) model is proposed for wave height prediction. The model effectively extracts relevant features such as wind speed, wind direction, wave height, latitude, and longitude. The proposed model outperforms traditional machine learning algorithms such as multi-layer perceptron (MLP), support vector machine (SVM), random forest and LSTM, especially for extreme values and fl…

    A novel convolutional neural network-long short-term memory (CNN-LSTM) model is proposed for wave height prediction. The model effectively extracts relevant features such as wind speed, wind direction, wave height, latitude, and longitude. The proposed model outperforms traditional machine learning algorithms such as multi-layer perceptron (MLP), support vector machine (SVM), random forest and LSTM, especially for extreme values and fluctuations. The model has a significantly lower average root mean square error (RMSE) of 71.1%, 72.8%, 71.9% and 72.2% for MLP, SVM, random forest and LSTM, respectively. Our model is computationally more efficient than traditional numerical simulations, making it suitable for real-time applications. Moreover, it has better long-term robustness compared to traditional models. The integration of CNN and LSTM techniques improves wave height prediction accuracy while enhancing its efficiency and robustness. The proposed CNN-LSTM model provides a promising tool for effective wave height prediction, making a valuable contribution to coastal disaster prevention and mitigation. Future research should aim to improve long-term prediction accuracy, and we believe that the CNN-LSTM model plays a crucial role in developing real-time coastal disaster prevention and mitigation measures. Overall, our study represents a significant step towards achieving more accurate and efficient wave height prediction using machine learning techniques.

    ScienceDirect

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    A novel hybrid new model is proposed for wave height prediction.

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    The prediction accuracy of the new model is higher than other models.

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    The new model is effective in recognizing spatial issue in wave forecasting.

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    Significant wave height

    Convolutional Neural Networks

    Long Short-Term Memory

    Deep learning

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    Measuring nearshore waves is crucial for coastal protection and carrying out operations along the coast, such as harbour and dock design, flood protection measures, and economic development in coastal areas (Ardhuin et al., 2007). However, traditional observation methods require significant manpower and financial resources. Therefore, scholars both domestically and internationally have developed various models to simulate the propagation of nearshore waves. These models are primarily divided into semi-empirical models based on measured data and theoretical derivation (Young and Verhagen, 1996), and numerical simulation models based on numerical methods and software (Booij et al., 1999, Parker and Hill, 2017, Tolman, 1991).

    In the past few decades, significant progress has been made in wave numerical simulation models based on the principles of wave dynamics and momentum conservation. Physically-based engineering wave forecasting models have been successively proposed, among which well-known ones include WAM (Komen et al., 1996), WAVEWATCHCIII (Tolman, 1991), and SWAN (Booij et al., 1999). These models have been widely applied in wave simulation (Samiksha et al., 2021, Zijlema, 2010), vegetation attenuation (Suzuki et al., 2012, Suzuki et al., 2019), storm surge modeling (Sebastian et al., 2014), and wave energy prediction (Lu et al., 2022, Ali et al., 2021), and other fields.

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    The wind field data were obtained from the ERA-5 reanalysis dataset of ECMWF (www.ecmwf.int) with a spatial resolution of 0.25° × 0.25° and a frequency of 3 h per interval, covering the period from 1994 to 2016. The wave data were obtained from the ERA-5 reanalysis dataset of the National Oceanographic Data Center (http://mds.nmdis.org.cn/) and ECM...

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