한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제63권5호
  • /
  • Pages.83-93
  • /
  • 2021
  • /
  • 1738-3692(pISSN)
  • /
  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
권호 표지
DOI QR코드

DOI QR Code

필터링 기법을 이용한 농업용저수지 수위자료의 품질관리 방안

Quality Control on Water-level Data in Agricultural Reservoirs Considering Filtering Methods

  • Kim, Kyung-hwan (Jeonnam Regional Headquarter, Korea Rural Community Corporation (KRC)) ;
  • Choi, Gyu-hoon (WeDB company) ;
  • Jung, Hyoung-mo (Agricultural Infrastructure Project Office, Korea Rural Community Corporation (KRC)) ;
  • Joo, Donghyuk (Department of Rural and Bio-Systems Engineering & BK21 Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture, Chonnam National University) ;
  • Na, Ra (Department of Rural and Bio-Systems Engineering & BK21 Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture, Chonnam National University) ;
  • Choi, Eun-hyuk (Rural Research Institute, Korea Rural Community Corporation (KRC)) ;
  • Kwon, Jae-Hwan (Agricultural Infrastructure Project Office, Korea Rural Community Corporation (KRC)) ;
  • Yoo, Seung-Hwan (Department of Rural and Bio-Systems Engineering & BK21 Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture, Chonnam National University)
  • 투고 : 2021.05.26
  • 심사 : 2021.09.17
  • 발행 : 2021.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Agricultural reservoirs are important facilities for storing or managing water for the purpose of securing agricultural water, creating and expanding agricultural production bases, and using them to increase agricultural production. In particular, the Korea Rural Community Corporation (KRC) manages agricultural reservoirs scattered across the country, and officially recognizes and distributes hydrological data to increase their public utilization and aims to improve the value of water resources. Data on the water level of agricultural reservoirs are important. However, errors such as missing values and outliners limit utilization of the data in various fields of research and industry. Therefore, water quality data measures should be devised to increase reliability. this study categorized different error types and looked at automatic correction methods to enhance the reliability of the vast hydrological data. In addition, the water level data corrected from errors were compared to the reference hydrologic data through expert judgment in accordance with the quality control procedure, and the most appropriate measures were verified. As KRC manages more agricultural reservoirs than any other institution, the proposed method of efficient and automatic water level data correction in this study is expected to increase the availability and reliability of the hydrological data.

키워드

참고문헌

  1. Gu, M. R., K. S. Lee, and D. S. Kang, 2010. Image noise reduction using modified gaussian filter by estimated standard deviation of noise. Journal of the Korean Institute of Information Technology, 8(12): 111-117.
  2. Kim, M., J. Y. Choi, J. H. Bang, and J. J. Lee, 2019. Outlier detection of real-time reservoir water level data using threshold model and artificial neural network model. Journal of the Korean Society of Agricultural Engineers, 61(1): 107-120. https://doi.org/10.5389/KSAE.2019.61.1.107
  3. Kim, C. S., H. S. Kim, H. S. Cho, and H. R. Kim, 2008. Establishment of national quality control system for the hydrologic data. Journal of the Korea Water Resources Association, 1823-1827.
  4. KMA, 2006. Real-time quality control system for meteorological observation data (I) Application. 11-1360000 -000206-01 (Tech. Note 2006-2): 157.
  5. Korea Rural Community Corporation (KRC), 2018. Development of technology for securing reliability of water level measurement data and estimating water supply.
  6. Korea Rural Community Corporation (KRC), 2019. A study on the establishment of quality control standards for hydrologic data in agricultural reservoirs and waterways.
  7. Korea Rural Community Corporation (KRC), 2020. Advanced quality management study of reservoir water level measurement data.
  8. K-water, 2018. K-water data quality management guidelines (water information).
  9. Lee, J. S., 2006. Hydrology, 305.
  10. Lee, J. S., 2006. Hydrology, 447-480.
  11. MathWorks. https://kr.mathworks.com.
  12. Ministry of Agriculture, Food and Rural Affairs, 2016. Development of information analysis technology for abnormal behavior of agricultural reservoirs using big data related to weather information and water levels.
  13. Ministry of Environment, 2018. Environment and maintenance and management of hydrologic research facilities and standards for quality management of hydrologic data.
  14. Ministry of the Interior and Safety, 2018. Public data quality management manual ver 2.0.
  15. Ministry of Land, Transport and Maritime Affairs, 2008. Establishment of a basic plan for hydrologic investigation (2010-2019).
  16. Ministry of Land, Transport and Maritime Affairs, 2011. The 4th Comprehensive Plan for Water Resources 2nd Amendment (2011-2020).
  17. Ministry of Land, Infrastructure and Transport, 2018. Establishment and operation of the national hydrologic data quality management system (7th).
  18. Oh, J. W., J. H. Park, and Y. K. Kim, 2008. Missing hydrological data estimation using neural network and real time data reconciliation. Journal of the Korea Water Resources Association 41(10): 1059-1065. doi:10.3741/JKWRA.2008.41.10.1059.
  19. Shin, H. J., and T. H. Lee, 2020. A study on the estimation of missing hydrological data using adaptive network-based fuzzy inference system (ANFIS). Journal of the Korea Water Resources Association, 1738-2726.
  20. Yang, M. H., W. H. Nam, H. J. Kim, T. G. Kim, A. K. Shin, and M. S. Kang, 2020. Anomaly detection in reservoir water level data using the LSTM model based on deep learning. Journal of the Korean Society of Hazard Mitigation, 21(1): 71-81. doi:10.9798/KOSHAM.2021.21.1.71.