시계열 분석 딥러닝 알고리즘을 적용한 낙동강 하굿둑 염분 예측
Prediction of Salinity of Nakdong River Estuary Using Deep Learning Algorithm (LSTM) for Time Series Analysis
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초록
낙동강 하굿둑은 올해 2022년 해수 유입기간을 매월 대조기마다로 확대, 하굿둑 상류 15 km 이내로 기수역조성을 목표로 운영되고 있다. 목표 기수역 조성구간 및 염수피해 방지를 위한 신속한 의사결정을 위해 본 연구에서는 딥러닝 알고리즘 Long Short-Term Memory(LSTM)을 적용하여 낙동대교(하굿둑 상류 약 5 km)지점의 염분 예측을 수행하였다. 창녕·함안보 방류량 등 낙동강 하구역의 시·공간적 특성을 반영하기 위한 입력데이터를 구축하였으며, Sequence length에 따른 정도 변화를 통해 낙동강 하구역의 수리학적 특성을 고려한 최적모델을 구축하였다. 예측정확도는 결정계수(R-squred)와 RMSE(root mean square error) 이용하여 통계분석을 실시하였으며. Sequence length 가 12일 때 R-squred 0.997, RMSE 0.122로 가장 정도가 높았으며, 선행 예측시간은 12시간 간격까지 R -squred 0 .93 이상으로 높은 정도를 보였다.
Abstract
Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.
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