This study introduces a novel fusion deep learning framework that integrates a convolutional neural network (CNN), a bidirectional gated recurrent unit (BiGRU), and a self-attention (SA) mechanism to address the shortcomings of conventional linear models in modeling and predicting nonlinear dynamics of sea level changes. To further enhance model adaptability and performance, the Dream Optimization Algorithm (DOA) is incorporated to enable hyperparameter tuning, resulting in the DOA-CNN-BiGRU-SA framework, which significantly improves the model’s ability to predict nonlinear sea level time series. To mitigate the impact of randomness in neural network initialization, we initially employed a default random seed and conducted experiments with data from five tidal stations in Japan. The DOA-CNN-BiGRU-SA framework outperformed seven other relevant models. Subsequently, an extended evaluation was carried out using data from six additional tidal stations, with predictions generated across 30 different random seeds, confirming the model’s competitive accuracy and robustness. Finally, the proposed framework was applied to satellite altimetry data over the entire East and South China Sea region. Two distinct processing strategies yielded regional sea level rise trends of 3.96 ± 0.47 mm/year and 4.02 ± 0.47 mm/year, respectively, over the 1993–2023 period, and these results closely agree with those reported in the China Sea Level Bulletin report in 2023. This paper presents an integrated approach that enables joint optimization of deep learning architectures and investigates the effects of initialization randomness in neural networks, offering a robust technical solution for predicting short-term regional sea level changes.
Wu et al. (Tue,) studied this question.