121 / 2024-02-14 10:53:45

Analysis of non-unique stage–discharge relationship using a machine learning method

GRU, Stage–discharge relationship, Weir operation, Machine learning

River discharge is a key hydrological issue for the river and water resources management. Recently, climate change is likely exacerbating the frequency and intensity of the extreme flood events, which indicates continuous monitoring of water discharge and its variation at different time scales are of prime important, especially for large river basins. The stage–discharge relationship or rating curve in a river is very useful because it allows computing the discharges from measured water levels at a gauge station. A single-valued rating curve can be used for a nearly steady regime. However, a complex relationship between stage and discharge should be established when there is a non-stationary regime due to, for example, the operation of artificial constructions, such as dams and weirs. This study aims to evaluate the stage-discharge relationship considering weir operation. A machine learning architecture, gated recurrent unit (GRU), is developed to determine the complex relationship between water level and discharge at the Yeoju Bridge which is located between Gangcheon and Yeoju weirs. To consider both of the upstream and downstream weir operation, observed upstream and downstream water levels of individual weirs are included as GRU inputs. The root mean squared error (RMSE) is adopted to assess the GRU performance. Our findings show that the GRU model considering the effect of weir operation can estimate the discharge with satisfactory accuracy by establishing the relationship between stage and discharge. The approach introduced in this study enables the estimation of discharge in stream networks with abundant artificial constructions, such as weirs and estuary barrages, where streamflow is highly affected by their operations.