Optimization of Input Variables for Salinity Modeling in the Nakdong River Estuary Using Exploratory Data Analysis

The Nakdong River Estuary is a vital ecological and water resource facing management challenges due to seawater intrusion and seasonal hydrological changes. The continuous opening of the estuarine bank since 2022 has increased the need for accurate salinity prediction to support ecosystem restoration and water-resource management. This study aims to improve the reliability and efficiency of salinity prediction models by optimizing the input variables using exploratory data analysis. Multidimensional observational data collected from January 2019 to June 2022 were analyzed to identify the statistical characteristics and seasonal variability of key factors. Principal component analysis was used to select critical variables, and a data-driven variable selection process was implemented. Experiments using a long short-term memory deep learning model showed that the optimized variable set improved the prediction performance by approximately 4.5% compared to models using all available variables. These results demonstrate that a data-centric approach enables the development of robust and interpretable prediction models that can be utilized by non-specialists in environmental hydraulics. The findings are expected to expand the practical application of artificial intelligence technologies in environmental management and contribute to the development of data-driven decision-support systems in estuarine environments.