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Optimizing the Long Short-Term Memory (LSTM) model by Bayesian method for salinity intrusion forecasting: A study at Dai Ngai station, Soc Trang province, Vietnam
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Tải xuống
Salinity intrusion forecasting is essential and challenging for hydrometeorology, especially in climate change. Employing machine learning (ML) algorithms and conventional forecasting techniques are gaining popularity and providing high performance. This study presents a method to optimize a machine learning model based on the Long Short-Term Memory (LSTM) algorithm for multistep-ahead salinity forecasting (up to 7 days) at Dai Ngai station, Soc Trang province. |