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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 wasdoi:10.3390/w13202820 fatcat:6bv46jrxdbhtrb2faumclkqimi