Transfer learning of long short-term memory analysis in significant wave height prediction off the coast of western Tohoku, Japan

Obara, Yuki; Nakamura, Ryota

doi:10.1016/j.oceaneng.2022.113048

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…New domains can be used when they are similar to the old ones; i.e., transfer learning allows source domains to be different from target domains [49]. Therefore, transfer learning can eliminate the limitations of the learning model and alleviate model over-fitting to improve the accuracy [50][51][52] and enhance the generalization ability of the model.…”

Section: Transfer Learningmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

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The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends.

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Section: Transfer Learningmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

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“…Based on this, [17] proposed the LSTM-CFCC algorithm, which successfully achieved regional SST prediction using multiple LSTM units to model the grid point data separately. [18] combined migration learning and LSTM for significant wave height (SWH) prediction, modeling the SWH of several points separately and considering other influencing factors to improve the SWH prediction accuracy. [19] used an encoderdecoder structure and GRU as an encoder-decoder with a self-attention mechanism to assist in SST prediction.…”

Section: Introductionmentioning

confidence: 99%

StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention Networks

Lin,

Zhang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ocean weather prediction is crucial for various applications, such as global climate prediction, marine environmental protection, and offshore production. However, current data-based marine weather prediction methods have limitations when predicting multiple variables in a particular area, failing to meet the efficiency and accuracy requirements of practical applications. In the realm of ocean weather variations, the presence of highly interconnected spatial and temporal continuations, coupled with the mutual influence of individual variables, underscores the utmost importance of effectively capturing dynamic correlations encompassing space, time, and variables to accurately predict ocean weather. To address this, we developed a novel approach called StHCFormer, which is a multivariate spatiotemporal hybrid convolutional attention network. The first key component of StHCFormer is the spatiotemporal hybrid convolutional attention (StHCA) module, which leverages a hybrid convolutional attention mechanism to explore both global spatial representations and local features. Additionally, the module incorporates temporal attention to capture the temporal dependence of weather records and effectively captures the dynamic correlations among multiple variables through channel deflation and weighted residuals. To ensure balanced variable losses, we introduced the concept of homoscedasticity uncertainty loss to dynamically adjust the multitask weights. This guarantees a global optimal solution and leads to more accurate multivariate ocean weather prediction. Finally, we conducted a comprehensive evaluation and comparison of the StHCFormer model with other state-of-the-art algorithms using the ERA5 dataset in the Philippine Sea. The results demonstrated that StHCFormer outperforms existing methods in marine multivariate field weather prediction.

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“…Transfer learning is employed as a modeling strategy wherein a model trained on one data set (source model) is utilized to make predictions on another data set (target model). This approach enables the model to undergo update learning with small samples, thereby enhancing the adaptability of learning methods (Obara et al, 2022). This can be done in two ways: (a) fine-tuning the source model on the target dataset; (b) using the source model as a feature extractor to extract robust features for the target dataset to build the target model.…”

Section: Introductionmentioning

confidence: 99%

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

Jin,

Xu,

Zhang

et al. 2023

Front. Mar. Sci.

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Acoustic tracking of whales’ underwater cruises is essential for protecting marine ecosystems. For cetacean conservationists, fewer hydrophones will provide more convenience in capturing high-mobility whale positions. Currently, it has been possible to use two hydrophones individually to accomplish direction finding or ranging. However, traditional methods only aim at estimating one of the spatial parameters and are susceptible to the detrimental effects of reverberation superimposition. To achieve complete whale tracking under reverberant interference, in this study, an intelligent acoustic tracking model (CIAT) is proposed, which allows both horizontal direction discrimination and distance/depth perception by mining unpredictable features of position information directly from the received signals of two hydrophones. Specifically, the horizontal direction is discriminated by an enhanced cross-spectral analysis to make full use of the exact frequency of received signals and eliminate the interference of non-source signals, and the distance/depth direction combines convolutional neural network (CNN) with transfer learning to address the adverse effects caused by unavoidable acoustic reflections and reverberation superposition. Experiments with real recordings show that 0.13 km/MAE is achieved within 8 km. Our work not only provides satisfactory prediction performance, but also effectively avoids the reverberation effect of long-distance signal propagation, opening up a new avenue for underwater target tracking.

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Transfer learning of long short-term memory analysis in significant wave height prediction off the coast of western Tohoku, Japan

Cited by 9 publications

References 25 publications

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention Networks

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

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