Reservoir Inflow Forecast Using a Clustered Random Deep Fusion Approach in the Three Gorges Reservoir, China
Publication: Journal of Hydrologic Engineering
Volume 23, Issue 10
Abstract
Reservoir inflow forecast plays a crucial part in programming, development, operation, and management of water resource systems. To better reveal the complex properties of daily reservoir inflow, a clustered deep fusion (CDF) approach is proposed in this paper. First, variational mode decomposition (VMD) is used to decompose the daily reservoir inflow series into multiple modes, which are clustered into different sets by fuzzy -means according to the Xie-Beni index in view of attribute domain. In each cluster, a deep autoencoder model (DAE) is developed for deep representations of the attributes in the deep domain. DAE outputs are finally fused at the synthesis domain into the forecasting results using random forest (RF). In this way, the inflow time series may be successively observed in the attribute domain, deep domain, and synthesis domain, which results in a clearer understanding of reservoir inflow trend. The present approach is modeled and evaluated using historical data collected from the Three Gorges Reservoir, China. For comparison, two kinds of learning patterns—deep learning (VMD-DAE-RF and DAE) and shallow learning (feed-forward neural network, least-squares support regression, and RF)—are applied to the same case. The results indicate that the proposed CDF model outperforms all comparison models in terms of mean absolute percentage error (6.174%), root mean-square error (), and correlation coefficient criteria (0.987). Thus, it is concluded that deep learning in the cluster fusion architecture is more promising.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (51775112), the Postdoctoral Science Foundation of China (2016M602459), and the Research Program of Higher Education of Guangdong (2016KZDXM054, 2016KQNCX165). We also thank the anonymous referees for comments that have helped improve this paper.
References
Ay, M., and O. Kisi. 2014. “Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques.” J. Hydrol. 511: 279–289. https://doi.org/10.1016/j.jhydrol.2014.01.0540.
Bai, Y., Z. Q. Chen, J. J. Xie, and C. Li. 2016a. “Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models.” J. Hydrol. 532: 193–206. https://doi.org/10.1016/j.jhydrol.2015.11.011.
Bai, Y., P. Wang, J. J. Xie, J. T. Li, and C. Li. 2015. “An additive model for monthly reservoir inflow forecast.” J. Hydrol. Eng. 20 (7): 04014079. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001101.
Bai, Y., J. J. Xie, X. Wang, and C. Li. 2016b. “Model fusion approach for monthly reservoir inflow forecasting.” J. Hydroinf. 18 (4): 634–650. https://doi.org/10.2166/hydro.2016.141.
Basu, B., and V. V. Srinivas. 2014. “Regional flood frequency analysis using kernel-based fuzzy clustering approach.” Water Resour. Res. 50 (4): 3295–3316. https://doi.org/10.1002/2012WR012828.
Bengio, Y., P. Lamblin, D. Popovici, and H. Larochelle. 2007. “Greedy layer-wise training of deep networks.” Adv. Neural Inf. Process. Syst. 19: 153–160.
Bezdek, J. C. 1981. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.
Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. 2013. Time series analysis: Forecasting and control. Hoboken, NJ: Wiley.
Breiman, L. 2001. “Random forest.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Chau, C. M. 2011. “Wavelet-based multi-scale entropy analysis of complex rainfall time series.” Entropy 13 (1) : 241–253. https://doi.org/10.3390/e13010241.
Chen, Y., Z. Lin, X. Zhao, G. Wang, and Y. F. Gu. 2014. “Deep learning-based classification of hyperspectral data.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (6): 2094–2107. https://doi.org/10.1109/JSTARS.2014.2329330.
Cui, Q., X. Wang, C. H. Li, Y. P. Cai, and P. Y. Liang. 2015. “Improved Thomas-fiering and wavelet neural network models for cumulative errors reduction in reservoir inflow forecast.” J. Hydro-Environ. Res. 13: 134–143. https://doi.org/10.1016/j.jher.2015.05.003.
Dragomiretskiy, K., and D. Zosso. 2014. “Variational mode decomposition.” IEEE Trans. Signal Process 62 (3): 531–544. https://doi.org/10.1109/TSP.2013.2288675.
Dragomiretskiy, K., and D. Zosso. 2015. “Two-dimensional variational mode decomposition.” Lect. Notes Comput. Sci. 8932: 197–208.
Erhan, D., Y. Bengio, A. Courville, P. A. Manzagol, P. Vincent, and S. Bengio. 2010. “Why does unsupervised pre-training help deep learning?” J. Mach. Learn. Res. 11: 625–660.
Hinton, G. E., S. Osindero, and Y. W. Teh. 2006. “A fast learning algorithm for deep belief nets.” Neural Comput. 18 (7): 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527.
Hipni, A., A. Elshafie, A. Najah, O. A. Karim, A. Hussain, and M. Mukhlisin. 2013. “Daily forecasting of dam water levels: Comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS).” Water Resour. Manage. 27 (10): 3803–3823. https://doi.org/10.1007/s11269-013-0382-4.
Huang, C. H., L. Zhong, Y. H. Huang, G. Zhang, and X. L. Zhong. 2015. “A novel method for text recognition in natural scene based on sparse stacked autoencoder.” J. Comput. Inf. Syst. 11 (4): 1399–1406. https://doi.org/10.12733/jcis13441.
Jothiprakash, V., and A. S. Kote. 2014. “Effect of pruning and smoothing while using M5 model tree technique for reservoir inflow prediction.” J. Hydrol. Eng. 16 (7): 563–574. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000342.
Kim, H. S., R. Eykholt, and J. D. Salas. 1999. “Nonlinear dynamics, delay times, and embedding windows.” Physica D 127 (1–2): 48–60. https://doi.org/10.1016/S0167-2789(98)00240-1.
Kugiumtzis, D. 1996. “State space reconstruction parameters in the analysis of chaotic time series: The role of the time window length.” Physica D 95 (1): 13–28. https://doi.org/10.1016/0167-2789(96)00054-1.
Längkvist, M., L. Karlsson, and A. Loutfi. 2014. “A review of unsupervised feature learning and deep learning for time-series modeling.” Pattern Recognit. Lett. 42 (6): 11–24. https://doi.org/10.1016/j.patrec.2014.01.008.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
Li, C., Y. Bai, and B. Zeng. 2016a. “Deep feature learning architectures for daily reservoir inflow forecasting.” Water Resour. Manage. 30 (14): 5145–5161. https://doi.org/10.1007/s11269-016-1474-8.
Li, C., D. Cabrera, J. Oliveira, R. V. Sanchez, M. Cerrada, and G. Zurita. 2016b. “Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram.” Mech. Syst. Signal Process. 76–77: 157–173. https://doi.org/10.1016/j.ymssp.2016.02.064.
Li, C., R. V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. Vásquez. 2015. “Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis.” Neurocomputing 168: 119–127. https://doi.org/10.1016/j.neucom.2015.06.008.
Li, C., R. V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. Vásquez. 2016c. “Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals.” Mech. Syst. Signal Process. 76–77: 283–293. https://doi.org/10.1016/j.ymssp.2016.02.007.
Liaw, A., and M. Wiener. 2002. “Classification and regression by random forest.” R News 2–3: 18–22.
Lin, G. F., G. R. Chen, P. Y. Huang, and Y. C. Chou. 2009. “Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods.” J. Hydrol. 372 (1–4): 17–29. https://doi.org/10.1016/j.jhydrol.2009.03.032.
Lohani, A. K., N. K. Goel, and K. K. S. Bhatia. 2014. “Improving real time flood forecasting using fuzzy inference system.” J. Hydrol. 509: 25–41. https://doi.org/10.1016/j.jhydrol.2013.11.021.
Maheswaran, R., and R. Khosa. 2013. “Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River.” J. Hydroinf. 15 (3): 1022–1041. https://doi.org/10.2166/hydro.2013.135.
Maslova, I., A. Ticlavilca, and M. Mckee. 2015. “Adjusting wavelet-based multiresolution analysis boundary conditions for long-term streamflow forecasting.” Hydrol. Processes 30 (1): 57–74. https://doi.org/10.1002/hyp.10564.
Najafi, M. R., H. Moradkhani, and T. C. Piechota. 2012. “Ensemble streamflow prediction: Climate signal weighting methods vs. climate forecast system reanalysis.” J. Hydrol. 442–443: 105–116. https://doi.org/10.1016/j.jhydrol.2012.04.003.
Packark, N. H., and J. P. Crutchfield. 1980. “Geometry from a time series.” Phys. Rev. Lett. 45 (9): 712–716. https://doi.org/10.1103/PhysRevLett.45.712.
Rahhal, M. M. A., Y. Bazi, H. Alhichri, N. Alajlan, F. Melgani, and R. R. Yager. 2016. “Deep learning approach for active classification of electrocardiogram signals.” Inf. Sci. 345: 340–354. https://doi.org/10.1016/j.ins.2016.01.082.
Remesan, R., and J. Mathew. 2015. Hydrological data driven modelling. Berlin: Springer.
Tsai, M. J., R. J. Abrahart, N. J. Mount, and F. J. Chang. 2014. “Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan.” Hydrol. Processes 28 (3): 1055–1070. https://doi.org/10.1002/hyp.9559.
Valipour, M., M. E. Banihabib, and S. M. R. Behbahani. 2013. “Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir.” J. Hydrol. 476: 433–441. https://doi.org/10.1016/j.jhydrol.2012.11.017.
Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol. 2010. “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” J. Mach. Learn. Res. 11 (8) : 3371–3408.
Wang, W. C., K. W. Chau, D. M. Xu, and X. Y. Chen. 2015. “Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition.” Water Resour. Manage. 29 (8): 2655–2675. https://doi.org/10.1007/s11269-015-0962-6.
Wang, Y., S. Guo, H. Chen, and Y. Zhou. 2014. “Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir.” Stochastic Environ. Res. Risk Assess. 28 (3): 555–570. https://doi.org/10.1007/s00477-013-0772-4.
Wu, C. L., and K. W. Chau. 2011. “Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis.” J. Hydrol. 399 (3–4): 394–409. https://doi.org/10.1016/j.jhydrol.2011.01.017.
Xie, X. L., and G. A. Beni. 1991. “Validity measure for fuzzy clustering.” IEEE Trans. Pattern Anal. Mach. Intell. 13 (8): 841–847. https://doi.org/10.1109/34.85677.
Xu, W., C. Zhang, Y. Peng, G. T. Fu, and H. C. Zhou. 2014. “A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts.” Water Resour. Res. 50 (12): 9267–9286. https://doi.org/10.1002/2013WR015181.
Xu, W., J. Zhao, T. Zhao, and Z. Wang. 2015. “Adaptive reservoir operation model incorporating nonstationary inflow prediction.” J. Water Resour. Plann. Manage. 141 (8): 04014099. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000502.
Yang, T., A. A. Asanjan, M. Faridzad, N. Hayatbini, X. Gao, and S. Sorooshian. 2017a. “An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis.” Inf. Sci. 418–419: 302–316. https://doi.org/10.1016/j.ins.2017.08.003.
Yang, T., A. A. Asanjan, E. Welles, X. Gao, S. Sorooshian, and X. Liu. 2017b. “Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information.” Water Resour. Res. 53 (4): 2786–2812. https://doi.org/10.1002/2017WR020482.
Yang, T., X. Gao, S. Sorooshian, and X. Liu. 2016. “Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme.” Water Resour. Res. 52 (3): 1626–1651. https://doi.org/10.1002/2015WR017394.
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Journal of Hydrologic Engineering
Volume 23 • Issue 10 • October 2018
Copyright
©2018 American Society of Civil Engineers.
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Received: Nov 1, 2017
Accepted: Apr 23, 2018
Published online: Jul 18, 2018
Published in print: Oct 1, 2018
Discussion open until: Dec 18, 2018
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