In our intelligent society, water resources are being managed using vast amounts of
hydrological data collected through telemetric devices. Recently, advanced data quality control
technologies for data refinement based on hydrological observation history, such as big data and
artificial intelligence, have been studied. However, these are impractical due to insufficient verification
and implementation periods. In this study, a process to accurately identify missing and
false-reading data was developed to efficiently validate hydrological data by combining various
conventional validation methods. Here, false-reading data were reclassified into suspected and
confirmed groups by combining the results of individual validation methods. Furthermore, an
integrated quality control process that links data validation and reconstruction was developed. In
particular, an iterative quality control feedback process was proposed to achieve highly reliable data
quality, which was applied to precipitation and water level stations in the Daecheong Dam Basin,
South Korea. The case study revealed that the proposed approach can improve the quality control
procedure of hydrological database and possibly be implemented in practice.