A 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

Tien Giang Nguyen; Trung Duc Tran; Cong Thanh  Nguyen

doi:10.15625/1859-3097/18174

Author affiliations

Authors

Tien Giang Nguyen Faculty of Hydrology, Meteorology & Oceanography, VNU University of Science, Vietnam
Trung Duc Tran School of Civil and Environmental Engineering, University of Ulsan, Republic of Korea
Cong Thanh Nguyen Southern Regional Hydrometeorological Center, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/1859-3097/18174

Keywords:

Long Short-Term Memory (LSTM), Bayesian method, multistep-ahead salinity forecasting.

Abstract

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. The optimization method based on the Bayesian algorithm for hyperparameters optimization and input predictors optimization has been highly effective for predicting salinity with a lead time of 1 day to 7 days. Specifically, the forecast results evaluated by the R² and RMSE indexes both give satisfactory results on the test data set (with lead time from 1 day to 7 days, R² ranges from 0.9 to 0.54, and RMSE ranges from 0.27 to 0.53). This study is a premise for improving machine learning models for short-term and long-term salinity intrusion prediction in the Mekong delta and Vietnam.

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