摘要
Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning.Conventional rainfall-runoff analysis methods generally have used hydrologic models.Rainfall-runoff analysis should consider complex interactions in the water cycle process,including precipitation and evapotranspiration.In this study,rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself.The study was conducted in the Yeongsan River basin,which forms a large-scale agricultural area even after industrialization,as the study area.As the hydrology model,SWAT(Soil and Water Assessment Tool)was used,and for the deep learning method,a Long Short-Term Memory(LSTM)network was used among RNNs(Recurrent Neural Networks)mainly used in time series analysis.As a result of the analysis,the correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are performance indicators of the hydrological model,showed higher performance in the LSTM network.In general,the LSTM network performs better with a longer calibration period.In other words,it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.
