1 손흥구, "스마트 그리드에서의 시계열 군집분석을 통한 전력수요 예측 연구" 한국통계학회 29 (29): 193-203, 2016
2 최보승, "도시가스 수요량 예측을 위한 시계열 모형 개발" 한국통계학회 22 (22): 1019-1032, 2009
3 김화수, "가정용수 용도별 사용량의 통계적 특성 분석" 대한토목학회 28 (28): 603-614, 2008
4 권현한, "Wavelet Transform 방법과 SVM 모형을 활용한 상수도 수요량 예측기법 개발" 한국수자원학회 45 (45): 1187-1199, 2012
5 Farriansyah, A., "Water allocation computation model for river and multi-reservoir system with sustainability-efficiency-equity criteria" 10 (10): 1537-, 2018
6 MOLIT, "Water Vision (2001~2020)"
7 UN Water, "The united nations world water development report 2015, water for a sustainable world" UNESCO 2015
8 Cortes, C., "Support vector networks" 20 : 273-297, 1995
9 Khorasani, M., "Simulation and analysis of temporal changes of groundwater depth using time series modeling" 2 (2): 90-, 2016
10 Bougadis, J., "Short-term municipal water demand forecasting" 19 (19): 137-148, 2005
1 손흥구, "스마트 그리드에서의 시계열 군집분석을 통한 전력수요 예측 연구" 한국통계학회 29 (29): 193-203, 2016
2 최보승, "도시가스 수요량 예측을 위한 시계열 모형 개발" 한국통계학회 22 (22): 1019-1032, 2009
3 김화수, "가정용수 용도별 사용량의 통계적 특성 분석" 대한토목학회 28 (28): 603-614, 2008
4 권현한, "Wavelet Transform 방법과 SVM 모형을 활용한 상수도 수요량 예측기법 개발" 한국수자원학회 45 (45): 1187-1199, 2012
5 Farriansyah, A., "Water allocation computation model for river and multi-reservoir system with sustainability-efficiency-equity criteria" 10 (10): 1537-, 2018
6 MOLIT, "Water Vision (2001~2020)"
7 UN Water, "The united nations world water development report 2015, water for a sustainable world" UNESCO 2015
8 Cortes, C., "Support vector networks" 20 : 273-297, 1995
9 Khorasani, M., "Simulation and analysis of temporal changes of groundwater depth using time series modeling" 2 (2): 90-, 2016
10 Bougadis, J., "Short-term municipal water demand forecasting" 19 (19): 137-148, 2005
11 Barioni, L. G., "Report on modeldata comparison and improved model parameterisaion" INRA 59-, 2014
12 Barnett, M., "Real-time automation of water supply and distribution for the city of Jacksonville, Florida. USA" 9 (9): 15-29, 2004
13 Altunkaynak, A., "Monthly water consumption prediction using season algorithm and wavelet transform-based models" 143 (143): 04017011-1-04017011-10, 2017
14 MLTM, "Manual for the Permit-to-Use of River Water"
15 Amari, S., "Improving support vector machine classifiers by modifying kernel functions" 12 (12): 783-789, 1999
16 Gato, S., "Forecasting residential water demand: case study" 133 (133): 309-319, 2007
17 Bolouri-Yazdeli, Y., "Evaluation of real-time operation rules in reservoir systems operation" 28 (28): 715-729, 2014
18 Bai, Y., "Dynamic forecast of daily urban water consumption using a variable-structure support vector regression model" 14 (14): 04014058-, 2014
19 Goodfellow, I., "Deep learning" MIT press 2016
20 Meng, F., "Cost-effective river water quality management using integrated real-time control technology" 51 (51): 9876-9886, 2017
21 Candelieri, A., "Clustering and support vector regression for water demand forecasting and anomaly detection" 9 (9): 224-, 2017
22 Byun, H., "Applications of support vector machines for pattern recognition: a survey" Springer 213-236, 2002
23 De Jager, J. M., "Accuracy of vegetation evaporation ratio formulae for estimating final wheat yield" 20 : 307-314, 1994
24 Boser, B.E., "A training algorithm for optimal margin classifiers" ACM 144-152, 1992
25 Benitez, R., "A short-term data based water consumption prediction approach" 12 (12): 2359-, 2019
26 Jain, A., "A decision support system for drought characterization and management" 18 (18): 105-140, 2001
27 WMO, "2018 Annual Report, WMO for the Twenty-first Century" WMO 2019