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    Improving Water Salinity Forecasting in Bang Pakong River with Attention Mechanism
    (2024-12-02)
    Saksopit, Thongthai
    ;
    Khawne, Amnach
    Seawater intrusion in the Bang Pakong River estuary poses a significant threat to freshwater resources used for agriculture, municipal consumption, and industrial applications. Accurate prediction of salinity fluctuations is crucial for effective water management strategies. This study proposes an enhanced univariate salinity prediction method utilizing a Long Short-Term Memory (LSTM) model augmented with an Attention Mechanism. The Attention Mechanism empowers the LSTM to selectively focus on crucial information within extended historical salinity data sequences. The optimal input sequence length for the model is determined through a training process, aiming for the most accurate predictions. Here, the model forecasts salinity values 24 hours ahead and is evaluated against actual measurements. Performance metrics demonstrate that the Attention-LSTM model achieves the lowest error (MAE: 0.007834, MSE: 0.000094, RMSE: 0.009697, MAPE: 0.048736) and the highest accuracy (R<sup>2</sup>: 0.782927) at an input sequence length of 504 hours. These findings highlight the potential of the Attention-LSTM model for improved salinity prediction in the Bang Pakong River estuary, aiding water resource management strategies.
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    Combining attentional cnn and gru networks for ocean current prediction based on hf radar observations
    (2019-10-23)
    Thongniran, Nathachai
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Lawawirojwong, Siam
    ;
    Srestasathiern, Panu
    ;
    Vateekul, Peerapon
    Lately, CNN-GRU demonstrates the ability of deep learning techniques on ocean surface current prediction. Improvement of the current prediction model creates positive impact on variety of marine activities, such as search-and-rescue, disaster monitoring and power forecasting. Deep learning techniques was successfully deployed to improve model performance in many areas due to their ability to handle enormous amounts of information in a variety of inputs and their huge growth in recent years. Latest ocean current prediction employed a combination of two mature techniques, which are Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), to capture spatial and temporal characteristics of its nature. However, there is still room for improvement due to many modern techniques that still have not been employed, and domain knowledge in oceanic, such as lunar illumination, is not taken into account to improve prediction performance. This paper introduces the ocean surface prediction model that employs soft attention mechanism, transfer learning, and incorporation of domain knowledge inputs which are month number, lunar effect, and hour number. An experimental dataset from 2014 to 2016, provided by GISTDA, is collected by using high frequency (HF) radar stations located along the coastal Gulf of Thailand. The experiment compares an existing CNN-GRU and our proposed model. The result shows an improvement of the prediction model in terms of RMSE by 2.57%, and 3.44% on U and V components.