Streamflow measurements provide valuable hydrological information but, at the same time, are difficult to obtain. For this reason, discharge records of regular intervals are usually obtained indirectly by a stage–discharge rating curve, which establishes a relation between measured water levels to volumetric rate of flow. Rating curves are difficult to develop because they require simultaneous measurements of discharge and stage over a wide range of stages. Furthermore, the shear forces generated during flood events often change the streambed shape and roughness. As a result, over long periods, the stage–discharge measurements are likely to form clusters to which different stage–discharge rating curves apply. For the identification of these clusters, various robust statistical approaches have been suggested by researchers, which, however, have not become popular among practitioners because of their complexity. Alternatively, various researchers have employed machine learning approaches. These approaches, though motivated by the time-dependent nature of the rating curves, handle the data as of stationary origin. In this study, we examine the advantages of a very simple technique: use time as one of the machine learning model inputs. This approach was tested in three real-world case studies against a statistical method and the results indicated its potential value in the development of a simple tool for rating curves suitable for practitioners.
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