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2019 Vol.52, Issue 12 Preview Page

Research Article

31 December 2019. pp. 1075-1086
Abstract
References
1
Altunkaynak, A., and Nigussie, T.A. (2017). "Monthly water consumption prediction using season algorithm and wavelet transform-based models." Journal of Water Resources Planning and Management, Vol. 143, No. 6, pp. 04017011-1-04017011-10.
10.1061/(ASCE)WR.1943-5452.0000761
2
Amari, S., and Wu, S. (1999). "Improving support vector machine classifiers by modifying kernel functions." In Proceedings of International Conference on Neural Networks, Vol. 12, No. 6, pp. 783-789.
10.1016/S0893-6080(99)00032-5
3
Bai, Y., Wang, P., Li, C., and Xie, J. (2014). "Dynamic forecast of daily urban water consumption using a variable-structure support vector regression model." Journal of Water Resources Planning and Management, Vol. 14, No. 3, pp. 04014058.
10.1061/(ASCE)WR.1943-5452.0000457
4
Barioni, L.G., Bellocchi, G., Touhami, H.B., Conant, R., Chang, J., Coltri, P.P., Hassen, A., Martin, R., Silvestri, S., Sicerly, J., Tesfamariam, E.H., and Viovy, N. (2014). Report on model-data comparison and improved model parameterisaion. INRA, France, p. 59 (hal-01611412).
5
Barnett, M., Lee, T., Jentgen, L., Conrad, S., Kidder, H., Woolschlager, J., and Groff, C. (2004). "Real-time automation of water supply and distribution for the city of Jacksonville, Florida." USA. EICA, Vol. 9, No. 3, pp. 15-29.
6
Benitez, R., Ortiz-Caraballo, C., Preciado, J.C., Conejero, J.M., Figueroa, F.S., and Rubio-Largo, A. (2019). "A short-term data based water consumption prediction approach." Energies, Vol. 12. No. 12, pp. 2359.
10.3390/en12122359
7
Bolouri-Yazdeli, Y., Haddad, O.B., Fallah-Mehdipour, E., and Mariño, M.A. (2014). "Evaluation of real-time operation rules in reservoir systems operation." Water resources management, Vol. 28, No. 3, pp. 715-729.
10.1007/s11269-013-0510-1
8
Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992). "A training algorithm for optimal margin classifiers." In COLT '92: Proceeding of the fifth annual workshop on Computational learning theory, ACM, New York, NY, USA, pp. 144-152.
10.1145/130385.130401
9
Bougadis, J., Adamowski, K., and Diduch, R. (2005). "Short-term municipal water demand forecasting." Hydrological Processes: An International Journal, Vol. 19, No. 1, pp. 137-148.
10.1002/hyp.5763
10
Byun, H., and Lee, S.W. (2002). "Applications of support vector machines for pattern recognition: a survey." International Workshop on Support Vector Machines. Springer, pp. 213-236.
10.1007/3-540-45665-1_17
11
Candelieri, A. (2017). "Clustering and support vector regression for water demand forecasting and anomaly detection." Water, Vol. 9, No. 3, p. 224.
10.3390/w9030224
12
Choi, B.S., Kang, H.C., Lee, K.Y., and Han, S.T. (2009). "A development of time-series model for city gas demand forecasting." Korean Journal of Applied Statistics, Vol. 22, No. 5, pp. 1019-1032.
10.5351/KJAS.2009.22.5.1019
13
Cortes, C., and Vapnik, V. (1995). Support vector networks. Machine Learning, Vol. 20, pp. 273-297.
10.1007/BF00994018
14
De Jager, J.M., (1994). "Accuracy of vegetation evaporation ratio formulae for estimating final wheat yield." Water SA, Vol. 20, pp. 307-314.
15
Farriansyah, A., Juwono, P., Suhartanto, E., and Dermawan, V. (2018). "Water allocation computation model for river and multi-reservoir system with sustainability-efficiency-equity criteria." Water, Vol. 10, No. 11, pp. 1537.
10.3390/w10111537
16
Gato, S., Jayasuriya, N., and Roberts, P. (2007). "Forecasting residential water demand: case study." Journal of Water Resources Planning and Management, Vol. 133, No. 4, pp. 309-319.
10.1061/(ASCE)0733-9496(2007)133:4(309)
17
Goodfellow, I., Bengio, Y., and Courville, A. (2016). "Deep learning." MIT press.
18
Jain, A., and Ormsbee, L.E. (2001). "A decision support system for drought characterization and management." Civil Engineering Systems, Vol. 18, No. 2, pp. 105-140.
10.1080/02630250108970296
19
Khorasani, M., Ehteshami, M., Ghadimi, H., and Salari, M. (2016). "Simulation and analysis of temporal changes of groundwater depth using time series modeling." Model. Earth Syst. Environ., Vol. 2, No. 2, p. 90.
10.1007/s40808-016-0164-0
20
Kim, H., Lee, D., Park, N., and Jung, K. (2008). "Analysis on statistical characteristics of household water end-uses." Journal of the Korean Society of Civil Engineers, Vol. 28, No. 5, pp. 603-614.
21
Kwon, H., Kim, M., and Kim, W. (2012). "A development of water demand forecasting model based on Wavelet transform and Support vector machine." Journal of Korea Water Resources Association, Vol. 45, No. 11, pp. 1187-1199.
10.3741/JKWRA.2012.45.11.1187
22
Meng, F., Fu, G., and Butler, D. (2017). "Cost-effective river water quality management using integrated real-time control technology." Environmental science & technology, Vol. 51, No. 17, pp. 9876-9886.
10.1021/acs.est.7b0172728783322
23
MLTM (2009). Manual for the Permit-to-Use of River Water.
24
MOLIT (2016). Water Vision (2001~2020).
25
Sohn, H., Jung, S., and Kim, S. (2016). "A study on electricity demand forecasting based on time series clustering in smart grid." The Korean Journal of Applied Statistics, Vol. 29, No. 1, pp. 193-203.
10.5351/KJAS.2016.29.1.193
26
UN Water (2015). "The united nations world water development report 2015, water for a sustainable world." UNESCO, Paris, France.
27
WMO (2019). "2018 Annual Report, WMO for the Twenty-first Century." WMO, Switzerland.
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 52
  • No :12
  • Pages :1075-1086
  • Received Date : 2019-10-29
  • Revised Date : 2019-11-03
  • Accepted Date : 2019-11-09