Baseflow Separation for Improving Dam Inflow Prediction Using Data-Driven Models: A Case Study of Four Dams in South Korea

초록

Improving the accuracy of rainfall-runoff simulations is an important challenge for efficient water resource management. Data-driven models are alternatives that have been used to simulate and predict streamflow based on the relationships between meteorological variables and runoff. Therefore, to improve runoff forecasting performance, we created data-driven model-based runoff forecasting algorithms coupled with a baseflow separation process. For the evaluation, we used two types of data-driven algorithms, deep neural network (DNN) and random forest (RF), and considered the historical patterns of precipitation, air temperature, humidity, and dam inflows as the input data. In addition, we evaluated the prediction model by applying lead times of 1-7 days to construct the optimal input datasets. The dam inflow prediction using data-driven models coupled with the baseflow separation process performed more accurately than that of the algorithms without the added process. The results of this study suggest the role of baseflow in dam inflow prediction using a data-driven model, and it is expected that this will serve as an important resource for future dam management.

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