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Prediction of Dissolved Oxygen Factor at Oncheon Stream Watershed Using Long Short-Term Memory Algorithm
Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management.
Prediction of Dissolved Oxygen Factor at Oncheon Stream Watershed Using Long Short-Term Memory Algorithm
Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management.
Prediction of Dissolved Oxygen Factor at Oncheon Stream Watershed Using Long Short-Term Memory Algorithm
Heesung Lim (author) / Hyungjin Shin (author) / Jaenam Lee (author) / Jongwon Do (author) / Inhyeok Song (author) / Youngkyu Jin (author)
2024
Article (Journal)
Electronic Resource
Unknown
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