Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

Arash, A.M., Fryirs, K., and Ralph, T.J. (2025). “Protocol for extracting flow hydrograph shape metrics for use in time-series flood hydrology analysis.” PLoS ONE, Vol. 20, No. 1, e0315796.

Baek, J., Kim, C., Lee, K., Kang, H., and Song, J. (2020). “Application of microwave water surface current meter for measuring agricultural water intake.” Journal of Korea Water Resources Association, Vol. 53, No. 12, pp. 1071-1079.

Chen, Z., Ni, X., Li, H., and Kong, X. (2023). “FedLGAN: A method for anomaly detection and repair of hydrological telemetry data based on federated learning.” PeerJ Computer Science, Vol. 9, e1664.

Costa, J.E., Cheng, R.T., Haeni, F.P., Melcher, N., Spicer, K.R., Hayes, E., Plant, W., Taegue, C., and Barrick, D. (2006) “Use of radars to monitor stream discharge by noncontact methods.” Journal of Water Resources Research, AGU, Vol. 42, No. 7, W07422.

Fileni, F., Fowler, H.J., Lewis, E., McLay, F., and Yang, L. (2023). “A quality-control framework for sub-daily flow and level data for hydrological modelling in Great Britain.” Hydrology Research, Vol. 54, No. 11, pp. 1357-1367. doi: 10.2166/nh.2023.045.

Fulton, J.W., Mason, C.A., Eggleston, J.R., Nicotra, M.J., Chiu, C.-L., Henneberg, M.F., Best, H.R., Cederberg, J.R., Holnbeck, S.R., and Lotspeich, R.R., et al. (2020). “Near-field remote sensing of surface velocity and river discharge using radars and the probability concept at 10 U.S. Geological Survey streamgages.” Remote Sensing, Vol. 12, No. 8, 1296.

Haq, I.U., Lee, B.S., Rizzo, D.M., and Perdrial, J.N. (2024). “An automated machine learning approach for detecting anomalous peak patterns in time series data from a research watershed in the northeastern United States critical zone.” Machine Learning with Applications, Vol. 16, 100543.

Harmel, R.D., Cooper, R.J., Slade, R.M., Haney, R.L., and Arnold, J.G. (2006). “Cumulative uncertainty in measured streamflow and water quality data for small watersheds.” Transactions of the ASABE, Vol. 49, No. 3, pp. 689-701.

Hofkes, M., Nychka, D., Cath, T., and Hering, A.S. (2024). “Hybrid smoothing for anomaly detection in time series.” Technometrics, Vol. 68, No. 2, pp. 1-19.

Huang, Y., Chen, H., Liu, B., Huang, K., Wu, Z., and Yan, K. (2023). “Radar technology for river flow monitoring: Assessment of the current status and future challenges.” Water, Vol. 15, No. 10, 1904.

Islam, R. and Zhu, D. (2013). “Kernel density-based algorithm for despiking ADV data.” Journal of Hydraulic Engineering, Vol. 139, No. 7, pp. 785-793

Jesson, M., Sterling, M., and Bridgeman, J. (2013). “Despiking velocity time-series - Optimisation through the combination of spike detection and replacement methods.” Flow Measurement and Instrumentation, Vol. 30, pp. 45-51

Joo, D., Na, R., Kim, H., Choi, G., Kwon, J., and Yoo, S. (2022). “Analysis of the optimal window size of Hampel Filter for calibration of real-time water level in agricultural reservoirs.” Journal of The Korean Society of Agricultural Engineers, Vol. 64, No. 3, pp. 9-24.

Kuhaneswaran, B., Sorwar, G., Alaei, A.R., and Tong, F. (2026). “Feature-enhanced erroneous outlier detection in hydrological time series using ensemble methods.” Water, Vol. 18, No. 4, 446.

Lee, S., Lee, K., Ryu, Y., Lee, J., and Park, S. (2025) “Calculation of surface-mean velocity conversion coefficient in natural rivers.” Journal of Korea Water Resources Association, Vol. 58, No. 9, pp. 757-769.

Li, Y., Ryu, D., Western, A.W., and Wang, Q.J. (2015). “Assimilation of streamdischarge for flood forecasting: Updating a semidistributed modelwith an integrated data assimilationscheme.” Water Resources Research, AGU, Vol. 51, No. 5, pp. 3238-3258.

Moraitis, G., and Makropoulos, C. (2024). “A hybrid dynamic graph neural network framework for real-time anomaly detection.” Journal of Hydroinformatics, Vol. 26, No. 12, pp. 3172-3191.

Parsheh, M., Sotiropoulos, F., and Porté-Agel, F. (2010). “Estimation of power spectra of acoustic-doppler velocimetry data contaminated with intermittent spikes.” Journal of Hydraulic Engineering, Vol. 136, No. 6, pp. 368-378.

Shabbir, M., Chand, S., and Iqbal, F. (2024). “Novel hybrid and weighted ensemble models to predict river discharge series with outliers.” Kuwait Journal of Science, Vol. 51, No. 2, 100188.

Song, J., Park, S., Kim, C., and Kim, H. (2023). “Application and comparative analysis of river discharge estimation methods using surface velocity.” Journal of Korean Society of Disaster and Security, Vol. 16, No. 2, pp. 15-32.

Toribio, M.R., Menéndez, A.G., Toribio, S.R., Gracia, I.Á. (2025). “A novel approach to speed up Hampel Filter for outlier detection.” Sensors, Vol. 25, No. 11, 3319.

Turnipseed, D.P., and Sauer, V.B. (2010). Discharge measurements at Gaging Stations. Book 3, Chap. A8, U.S. Geological Survey, Reston, VA, U.S., pp. 29-86.

Wahl, T.L. (2003). “Discussion of “Despiking acoustic doppler velocimeter data” by Derek G. Goring and Vladimir I. Nikora.” Journal of Hydraulic Engineering, Vol. 128, No. 1, pp. 117-126.

Wang, H.-W., Lin, G.-F., Tfwala, S.S., and Hong, J.-H. (2019). “Filtering continuous river surface velocity radar data.” Water, Vol. 11, No. 4, 764.

Wendi, D., Merz, B., and Marwan, N. (2019). “Assessing hydrograph similarity and rare runoff dynamics by cross recurrence plots.” Water Resources Research, AGU, Vol. 55, No. 6, pp. 4704-4726.

Zhang, G., Cui, P., Yin, Y., Liu, D., Jin, W., Yan, Y., Ahmed, B.N., Wang, J. (2019) “Real-time monitoring and estimation of the discharge of flash floods in a steep mountain catchment.” Hydrological Processes. Vol. 33, No. 25, pp. 3195-3212.

Zhou, Z., Riis-Klinkvort, L., Jørgensen, E.A., Lindenhoff, C., Frías, M.C., Vesterhauge, A.R., Olesen, D.H., Lavish, M., Dobrovolskiy, A., and Kadek, A. et al. (2024). “Measuring river surface velocity using UAS-borne Doppler radar.” Water Resources Research, Vol. 60, No. 11, e2024WR037375.