3. Choi, H.S, and Kim, J.Y (2009) Quality control and improvement of hydrologic data at the Geum river flood control center.
Magazine of Korea Water Resources Association, Vol. 42, No. 2, pp. 49-54.
4. Hochreiter, S, and Schmidhuber, J (1997) Long short-term memory.
Neural Computation, Vol. 9, No. 8, pp. 1735-1780.
5. Hong, E.M, Nam, W.H, Choi, J.Y, and Kim, J.T (2014) Evaluation of water supply adequacy using real-time water level monitoring system in paddy irrigation canals.
Journal of the Korean Society of Agricultural Engineers, Vol. 56, No. 4, pp. 1-8.
6. Jang, W, Lee, Y, Lee, J, and Kim, S (2019) RNN-LSTM based soil moisture estimation using Terra MODIS NDVI and LST.
Journal of the Korean Society of Agricultural Engineers, Vol. 61, No. 6, pp. 123-132.
7. Jeon, B.K, Lee, K.H, and Kim, E.J (2019) Development of a prediction model of solar irradiances using LSTM for use in building predictive control.
Journal of the Korean Solar Energy Society, Vol. 39, No. 5, pp. 41-52.
8. Jung, S.H, Lee, D.E, and Lee, K.S (2018) Prediction of river water level using deep-learning open library.
J. Korean Soc. Hazard Mitig, Vol. 18, No. 1, pp. 1-11.
9. Kang, M.G, Jeong, H.S, and Kim, J.T (2010) Efficient management of agricultural canal systems through quality management of water level and water quantity data.
Magazine of Korean Society of Agricultural Engineers, Vol. 52, No. 2, pp. 87-96.
10. Kim, D.S, Kang, S.M, Kim, J.T, Kim, J.D, Kim, H.H, and Jang, J.U (2017) Development and implementation of prototype for intelligent integrated agricultural water management information system and service including reservoirs managed by city and county.
Journal of the Korean Society of Rural Planning, Vol. 23, No. 3, pp. 163-174.
11. Kim, Y.H, Hwang, Y.K, Kang, T.G, and Jung, K.M (2016) LSTM language model based Korean sentence generation.
The Journal of Korean Institute of Communications and Information Sciences, Vol. 41, No. 5, pp. 592-601.
12. Korea Rural Community Corporation (KRC) (2018)
Technical development for data management and utilization using automatic water level measurements.
13. Korea Water Resources Corporation (K-water) (2017)
Development of automatic detection and refining system for abnormal data.
14. Korea Water Resources Corporation (K-water) (2019)
Development of quality control algorithm for standard database of water information.
15. Lee, T.H, and Jun, M.J (2018) Prediction of Seoul house price index using deep learning algorithms with multivariate time series data.
SH Urban Research &Insight, Vol. 8, No. 2, pp. 39-56.
16. Ministry of Agriculture, Food and Rural Affairs (MAFRA) (2014)
A study on the improvement of cost-bearing system for using and managing agricultural water and repair facilities.
17. Ministry of Land, Infrastructure and Transport (MOLIT) (2010)
A study on standardization of hydrological investigation methods and standards.
18. Ministry of Land, Infrastructure and Transport (MOLIT) (2011)
Guideline user guide for service Establishment of national hydrological data quality management system.
19. Nam, W.H, and Choi, J.Y (2013) Development of operation rules in agricultural reservoirs using real-time water level and irrigation vulnerability index.
Journal of the Korean Society of Agricultural Engineers, Vol. 55, No. 6, pp. 77-85.
20. Nam, W.H, Choi, J.Y, Hong, E.M, and Kim, J.T (2013) Assessment of irrigation efficiencies using smarter water management.
Journal of the Korean Society of Agricultural Engineers, Vol. 55, No. 4, pp. 45-53.
21. Nam, W.H, Hong, E.M, and Choi, J.Y (2016) Assessment of water delivery efficiency in irrigation canals using performance indicators.
Irrigation Science, Vol. 34, No. 2, pp. 129-143.
22. Oh, G.L, Lee, S.J, Choi, B.C, Kim, J, Kim, K.R, Choi, S.W, et al (2015) Quality control of agro-meteorological data measured at Suwon weather station of Korea Meteorological Administration.
Korean Journal of Agricultural and Forest Meteorology, Vol. 17, No. 1, pp. 25-34.
23. Oh, S.R, Kim, J.Y, Choe, Y.J, and Choe, H.J (2014) A study on the improvement of hydrologic data quality in water level observation sector.
Magazine of Korea Water Resources Association, Vol. 47, No. 7, pp. 74-80.
24. Ryu, B.H, and Han, C.S (2019) LSTM based hydraulic excavator angular velocity prediction model.
Journal of Institute of Control, Robotics and Systems, Vol. 25, No. 8, pp. 705-712.
25. Shin, D.H, Choi, K.H, and Kim, C.B (2017) Deep learning model for prediction rate improvement for stock price using RNN and LSTM.
Journal of Korean Institute of Information Technology, Vol. 15, No. 10, pp. 9-16.
26. Shin, J.H, Nam, W.H, Bang, N.K, Kim, H.J, An, H.U, Do, J.W, et al (2020) Assessment of water distribution and irrigation efficiency in agricultural reservoirs using SWMM model.
Journal of the Korean Society of Agricultural Engineers, Vol. 62, No. 3, pp. 1-13.
27. Yang, M.H, Nam, W.H, Kim, T, Lee, K, and Kim, Y (2019) Machine learning application for predicting the strawberry harvesting time.
Korean Journal of Agricultural Science, Vol. 46, No. 2, pp. 381-393.