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February 28, 2026Journal of the Korean Society for Marine Environment & Energy

A Deep Learning-Based Total Nitrogen Prediction Study in the Masan Bay Special Management Sea Area Using High-Resolution Continuous Observation Data

Jong-Hong Kim Chung-Sook Kim Junmo Jo Eun-Joo Lee Sung-Eun Park

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Key Points

The aim is to predict total nitrogen concentrations in Masan Bay using deep learning models based on high-resolution observation data.
Developed a long short-term memory (LSTM) model using water quality data from 2014 to 2024.
Conducted data preprocessing to enhance model accuracy.
Evaluated model performance based on coefficient of determination (R^2).
Initial model achieved a high accuracy with a coefficient of determination of 0.94.
Model performance declined during lead times and extreme rainfall events.
Post-preprocessing, the model improved to a coefficient of determination of 0.99.

Implication

Predicts total nitrogen levels in coastal waters, highlighting important implications for environmental management.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69b3aad702a1e69014ccba06 https://doi.org/10.7846/jkosmee.2026.29.1.79

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