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http://dx.doi.org/10.3741/JKWRA.2019.52.12.1075

Prediction on the amount of river water use using support vector machine with time series decomposition  

Choi, Seo Hye (Korea Institute of Civil Engineering and Building Technology)
Kwon, Hyun-Han (Department of Civil & Environmental Engineering, Sejong University)
Park, Moonhyung (Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korea Water Resources Association / v.52, no.12, 2019 , pp. 1075-1086 More about this Journal
Abstract
Recently, as the incidence of climate warming and abnormal climate increases, the forecasting of hydrological factors such as precipitation and river flow is getting more complicated, and the risk of water shortage is also increasing. Therefore, this study aims to develop a model for predicting the amount of water intake in mid-term. To this end, the correlation between water intake and meteorological factors, including temperature and precipitation, was used to select input factors. In addition, the amount of water intake increased with time series and seasonal characteristics were clearly shown. Thus, the preprocessing process was performed using the time series decomposition method, and the support vector machine (SVM) was applied to the residual to develop the river intake prediction model. This model has an error of 4.1% on average, which is higher accuracy than the SVM model without preprocessing. In particular, this model has an advantage in mid-term prediction for one to two months. It is expected that the water intake forecasting model developed in this study is useful to be applied for water allocation computation in the permission of river water use, water quality management, and drought measurement for sustainable and efficient management of water resources.
Keywords
The amount of water intake; Support vector machine; Time series decomposition; Prediction modeling; Drought;
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Times Cited By KSCI : 3  (Citation Analysis)
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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.   DOI
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.
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.
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 modeldata 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.   DOI
7 Bolouri-Yazdeli, Y., Haddad, O.B., Fallah-Mehdipour, E., and Marino, M.A. (2014). "Evaluation of real-time operation rules in reservoir systems operation." Water resources management, Vol. 28, No. 3, pp. 715-729.   DOI
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.
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.   DOI
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.
11 Candelieri, A. (2017). "Clustering and support vector regression for water demand forecasting and anomaly detection." Water, Vol. 9, No. 3, p. 224.   DOI
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.   DOI
13 Cortes, C., and Vapnik, V. (1995). Support vector networks. Machine Learning, Vol. 20, pp. 273-297.   DOI
14 Goodfellow, I., Bengio, Y., and Courville, A. (2016). "Deep learning." MIT press.
15 De Jager, J.M., (1994). "Accuracy of vegetation evaporation ratio formulae for estimating final wheat yield." Water SA, Vol. 20, pp. 307-314.
16 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.   DOI
17 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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
23 WMO (2019). "2018 Annual Report, WMO for the Twenty-first Century." WMO, Switzerland.
24 MLTM (2009). Manual for the Permit-to-Use of River Water.
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.   DOI
26 UN Water (2015). "The united nations world water development report 2015, water for a sustainable world." UNESCO, Paris, France.
27 MOLIT (2016). Water Vision (2001-2020).