과제정보
본 연구는 농림축산식품부의 재원 농림식품기술기획평가원의 농업기반 및 재해대응기술 개발사업(과제번호:320004-1)의 지원으로 수행되었습니다.
참고문헌
- Ahn SJ, Yeon IS, Han YS, Lee JK. 2001. Water quality forecasting at Gongju station in Geum River using neural network model. Journal of Korea Water Resources Association 34:701-711. [in Korean]
- Box GEP, Jenkins GM. 1970. Time series analysis, forecasting and control. pp. 109-118. WILEY, San Francisco, USA.
- Cho DH, Um HS. 2005. A study on the forecasting of water quality of Nakdong River - focusing on the Gorung measurement point using the monthly tme series data . Korea Environmental Policy and Administration Society 13:5-30. [in Korean]
- Han YS, Lee WH, Lee JK, Cho YJ. 2004. Time series analysis using black box model in dissolved. Proceedings of Korean Geo-Environmental Society 1:355-362. [in Korean]
- Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation 9:1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Huck PM, Farquhar GJ. 1974. Water quality models using the Box-Jenkins method. Journal of the Environmetal Engineering. Division 100:733-751. https://doi.org/10.1061/JEEGAV.0000192
- Jeong HJ, Lee SJ, Lee HK. 2002. Water quality forecasting of Chungju Lake using aritificial neural network algorithm. Journal of the Environmental Sciences 11:201-207. [in Korean] https://doi.org/10.5322/JES.2002.11.3.201
- Jung SH, Cho HS, Kim JY, Lee GH. 2018. Prediction of water level in a tidal River using a deep-learning based LSTM model. Journal Korea Water Resour 51:1207-2016. [in Korean]
- Kim DH, Kim JW, Kwak JW, Kim JS. 2020. Development of water level prediction models using deep neural network in mountain wetlands. Journal of Wetlands Researh 22:106-112. [in Korean]
- Kim JO, Yoo HH, Kim OS, Park JS. 1999. Forecasting of water quality in Chinyang Reservoir using ARIMA model. Journal of Wetlands Researh 1:17-28. [in Korean]
- K-water. 1996. A study on the prediction of pollutant advection and dispersion and on the reduction countermeasures in a large river: Focusing on the main stream of Nakdong River. K-water, Daejeon, Korea.
- Lim HS, An HU, Kim HD, Lee JJ. 2019. Prediction of pollution loads in the Geum River upstream using the recurrent neural network algorithm. Korean Journal of Agricultural Science 46:67-78. [in Korean] https://doi.org/10.7744/KJOAS.20180085
- Oh CR, Park SC, Lee HM, Pyo YP. 2002. A forecasting of water quality in the Youngsan River using neural network. Journal of the Korean Society of Civil Engineers B 22:371-382. [in Korean]
- Olah C. 2015. Understanding lstm networks. GITHUB blog. Accessed in http://colah.github.io/posts/2015-08-Understanding-LSTMs on 27 August 2015.
- Ryu BR, Jun KW. 2004. Forecasting of water quality using neural network at Gabcheon. Journal of Korean Society of Environmental Technology 5:231-240. [in Korean]
- Seo YM, Choi EH, Yeo WK. 2017. Reservoir water level forecasting using machine learning models. Journal of the Korean Society of Agricultural Engineers 59:97-110. [in Korean]
- Streeter HW, Phelps EB. 1925. A study of the pollution and natural purification of the Ohio River. pp. 1-75. Public Health Service, Washington, D.C., USA.
- Tran QK, Song S. 2017. Water level forecasting based on deep learning: A use case of Trinity River-Texas-The United States. Journal of Korean Institute of Information Scientists Engineers 44:607612. [in Korean]
- Wetzel. 1984. Liminology 2nd edition. Harcourt Brace Jovanovich Colleage Publishers, San Diego, USA.