Multiscale Characterization and Prediction of Reservoir Inflows Using MEMD-SLR Coupled Approach
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 1
Abstract
In this study, multiscale characterization of hydroclimatic time series was performed using the Hilbert-Huang Transform (HHT) approach. First, to investigate the possible teleconnections of monthly inflows into Hirakud reservoir in India, and two potential large-scale climate oscillations, El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), the corresponding time series were decomposed using the multivariate empirical mode decomposition (MEMD) method, and then running correlation analysis, namely time-dependent intrinsic correlation (TDIC) analysis, was applied. TDIC analysis showed that there exists a long-range negative correlation between EQUINOO and reservoir inflow in most of the time scales, whereas a positive relation prevails between ENSO and inflow at the biannual time scale in particular. TDIC analysis further proved that the association between large-scale climate oscillations and reservoir inflow is not always unique but associated with localized reversals in the nature of correlation in the time domain; also, both the nature and strength of the association vary with time scales. Stemming from this finding, this paper proposes an innovative approach that combines MEMD and stepwise linear regression (SLR) methods for prediction of reservoir inflows. In this approach, the different modes obtained through MEMD are predicted independently by SLR fitting, considering the statistically significant inputs at respective time scales, and the final summation of the predicted modes gives the monthly inflows. A statistical performance evaluation based on multiple criteria showed that the proposed MEMD-SLR approach displayed better performance, with a high of 0.905 and low values of root mean square error (RMSE) and bias for validation data over the EMD-SLR, M5 model tree, and multiple linear regression models for inflow prediction, including significant improvement in prediction of high inflows.
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References
Adarsh, S., and M. Janga Reddy. 2015. “Multiscale analysis of suspended sediment concentration data from natural channels using the Hilbert-Huang transform.” Aquat. Procedia 4: 780–788. https://doi.org/10.1016/j.aqpro.2015.02.097.
Adarsh, S., and M. Janga Reddy. 2016. “Analyzing the hydroclimatic teleconnections of summer monsoon rainfall in Kerala, India using multivariate empirical mode decomposition and time dependent intrinsic correlation.” IEEE Geosci. Remote Sens. Lett. 13 (9): 1121–1125. https://doi.org/10.1109/LGRS.2016.2577598.
Adarsh, S., and M. Janga Reddy. 2017. “Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis.” Meteorol. Atmos. Phys. 1–22. https://doi.org/10.1007/s00703-017-0545-6.
Anctil, F., and P. Coulibaly. 2004. “Wavelet analysis of the inter-annual variability in southern Québec streamflow.” J. Clim. 17 (1): 163–173. https://doi.org/10.1175/1520-0442(2004)017%3C0163:WAOTIV%3E2.0.CO;2.
Bai, Y., P. Wang, J. Xie, J. Li, and C. Li. 2015. “Additive model for monthly reservoir inflow forecast.” J. Hydrol. Eng. 20 (7): 04013079. https://doi.org/10.1061/(asce)he.1943-5584.0001101.
Biswas, A., and B.-C. Si. 2011. “Revealing the control of soil water storage in different scales in a hummocky landscape.” Soil Soc. Am. J. 75 (4): 1295–1306. https://doi.org/10.2136/sssaj2010.0131.
Chen, X., Z. Wu, and N. E. Huang. 2010. “The time-dependent intrinsic correlation based on the empirical mode decomposition.” Adv. Adapt. Data Anal. 2 (2): 233–265. https://doi.org/10.1142/S1793536910000471.
Derot, J., F. G. Schmitt, V. Gentilhomme, and P. Morin. 2016. “Correlation between long term marine temperature time series from eastern and western English channel: Scaling analysis using empirical mode decomposition method.” Comptes Rendus Geosci. 348 (5): 343–349. https://doi.org/10.1016/j.crte.2015.12.001.
Dickinson, J. E., R. T. Hanson, and S. K. Predmore. 2014. HydroClimATe-hydrologic and climatic analysis tool kit: US Geological Survey techniques and methods 4-A9, 49. Reston, VA: US Geological Survey.
Draper, N. R., and H. Smith. 1998. Applied regression analysis. 3rd ed. Wiley: New York.
Flandrin, P., G. Rilling, and P. Goncalves. 2004. “Empirical mode decomposition as a filter bank.” IEEE Signal Process. Lett. 11 (2): 112–114. https://doi.org/10.1109/LSP.2003.821662.
Franceschini, S., and C. Tsai. 2010. “Application of Hilbert-Huang transform method for analyzing toxic concentrations in the Niagara river.” J. Hydrol. Eng. 15 (2): 90–96. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000159.
Gadgil, S., P. N. Vinayachandran, P. A. Francis, and S. Gadgil. 2004. “Extremes of the Indian summer monsoon rainfall, ENSO and equatorial Indian Ocean oscillation.” Geophys. Res. Lett. 31 (12): L12213. https://doi.org/10.1029/2004GL019733.
Hu, J., J. Liu, Y. Liu, and C. Gao. 2013. “EMD-KNN model for annual average rainfall forecasting.” J. Hydrol. Eng. 18 (11): 1450–1457. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000481.
Hu, W., and B. C. Si. 2013. “Soil water prediction based on its scale-specific control using multivariate empirical mode decomposition.” Geoderma 193–194: 180–188. https://doi.org/10.1016/j.geoderma.2012.10.021.
Huang, G., Y. Su, A. Kareem, and H. Liao. 2016. “Time-frequency analysis of non-stationary process based on multivariate empirical mode decomposition.” J. Eng. Mech. 142 (1): 04015065. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000975.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. R. Soc. A. 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Huang, N. E., and Z. Wu. 2008. “A review on Hilbert Huang Transform: Method and its applications to geophysical studies.” Rev. Geophys. 46 (2): 1–23. https://doi.org/10.1029/2007RG000228.
Huang, N. E., Z. Wu, S. R. Long, K. C. Arnold, K. Blank, and T. W. Liu. 2009a. “On instantaneous frequency.” Adv. Adapt. Data Anal. 1 (2): 177–229. https://doi.org/10.1142/S1793536909000096.
Huang, S., J. Cheng, Q. Huang, and Y. Chen. 2014. “Monthly streamflow prediction using modified EMD-based support vector machine.” J. Hydrol. 511: 764–775. https://doi.org/10.1016/j.jhydrol.2014.01.062.
Huang, Y., and F. G. Schmitt. 2014. “Time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition.” J. Marine Syst. 130 (2014): 90–100. https://doi.org/10.1016/j.jmarsys.2013.06.007.
Huang, Y., F. G. Schmitt, Z. Lu, and Y. Liu. 2009b. “Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis.” J. Hydrol. 373 (1–2): 103–111. https://doi.org/10.1016/j.jhydrol.2009.04.015.
Ismail, D. K. B., P. Lazure, and I. Puillat. 2015. “Advanced spectral analysis and cross correlation based on empirical mode decomposition: Application to the environmental time series.” Geosci. Remote Sens. Lett. 12 (9): 1968–1972. https://doi.org/10.1109/LGRS.2015.2441374.
Janga Reddy, M., and S. Adarsh. 2016. “Time frequency characterization of subdivisional scale seasonal rainfall in India using Hilbert Huang transform.” Stochastic Environ. Res. Risk Assess. 30 (4): 1063–1085. https://doi.org/10.1007/s00477-015-1165-7.
Janga Reddy, M., and D. Nagesh Kumar. 2007. “Multi-objective differential evolution with application to reservoir system optimization.” J. Comp. Civ. Eng. 21 (2): 136–146. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:2(136).
Jothiprakash, V., and A. S. Kote. 2011. “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.
Karthikeyan, L., and D. Nagesh Kumar. 2013. “Predictability of non-stationary time series using Wavelet and EMD based ARMA models.” J. Hydrol. 502: 103–119. https://doi.org/10.1016/j.jhydrol.2013.08.030.
Kuai, K. Z., and C. W. Tsai. 2012. “Identification of varying time scales in sediment transport using the Hilbert-Huang transform method.” J. Hydrol. 420–421: 245–254. https://doi.org/10.1016/j.jhydrol.2011.12.007.
Liu, P. C. 1994. “Wavelet spectrum analysis and ocean wind waves.” In Wavelets in geophysics, edited by E. Foufoula-Georgiou, and P. Kumar, 151–166. Academic Press.
Maity, R., and D. Nagesh Kumar. 2006a. “Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño-Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO).” J. Geophy. Res. 111: D07104. https://doi.org/10.1029/2005JD006539.
Maity, R., and D. Nagesh Kumar. 2006b. “Hydroclimatic association of monthly summer monsoon rainfall over India with large-scale atmospheric circulation from tropical Pacific Ocean and Indian Ocean.” Atmos. Sci. Lett. 7 (4): 101–107. https://doi.org/10.1002/asl.141.
Maity, R., and D. Nagesh Kumar. 2007. “Basin-scale streamflow forecasting using the information of large-scale atmospheric circulation phenomena.” Hydrol. Process. 22 (5): 643–650. https://doi.org/10.1002/hyp.6630.
Maity, R., and D. Nagesh Kumar. 2009. “Hydroclimatic influence of large-scale circulation on the variability of reservoir inflow.” Hydrol. Process. 23 (6): 934–942. https://doi.org/10.1002/hyp.7227.
Massei, N., A. Durand, J. Deloffre, J. Dupont, D. Valdes, and B. Laignel. 2007. “Investigating possible links between the North Atlantic Oscillation and rainfall variability in north western France over the past 35 years.” J. Geophy. Res. Atmos. 112: 1–10. https://doi.org/10.1029/2005JD007000.
Massei, N., and M. Fournier. 2012. “Assessing the expression of large scale climatic fluctuations in the hydrologic variability of daily Seine river flow (France) between 1950-2008 using Hilbert Huang transform.” J. Hydrol. 448–449: 119–128. https://doi.org/10.1016/j.jhydrol.2012.04.052.
Massei, N., B. Laignel, E. Rosero, A. Motelay-Massei, J. Deloffre, Z. L. Yang, and A. Rossi. 2011. “A wavelet approach to the short-term to pluri-decennal variability of streamflow in the Mississippi river basin from 1934 to 1998.” Int. J. Climatol. 31 (1): 31–43. https://doi.org/10.1002/joc.1995.
Nagesh Kumar, D., M. Janga Reddy, and R. Maity. 2007. “Regional rainfall forecasting using large scale climate teleconnections and artificial intelligence techniques.” J. Intell. Syst. 16 (4): 307–322.
Napolitano, G., F. Serinaldi, and L. See. 2011. “Impact of EMD decomposition and random initialization of weights in ANN hindcasting of daily streamflow series: An empirical examination.” J. Hydrol. 406 (3–4): 199–214. https://doi.org/10.1016/j.jhydrol.2011.06.015.
Patri, S. 1993. Data on flood control operation of Hirakud dam. India: Irrigation Dept., Government of Orissa.
Quinlan, J. R. 1992. “Learning with continuous classes.” In Proc., Australian Joint Conf. on Artificial Intelligence, 343–348. Singapore: World Scientific Press.
Rao, A. R., and E. C. Hsu. 2008. Hilbert-Huang transform analysis of hydrological and environmental time series. Dordrecht, Netherlands: Springer.
Rehman, N., and D. P. Mandic. 2010a. “Empirical mode decomposition for tri-variate signals.” IEEE Trans. Signal Process. 58 (3): 1059–1068. https://doi.org/10.1109/TSP.2009.2033730.
Rehman, N., and D. P. Mandic. 2010b. “Multivariate empirical mode decomposition.” Proc. R. Soc. Ser. A. 466 (2117): 1291–1302. https://doi.org/10.1098/rspa.2009.0502.
Rilling, G., P. Flandrin, P. Goncalves, and J. M. Lilly. 2007. “Bi-variate empirical mode decomposition.” IEEE Signal Process. Lett. 14 (12): 936–939. https://doi.org/10.1109/LSP.2007.904710.
Rodo, X., and M. A. Rodriguez-Arias. 2006. “A new method to detect transitory signatures and local time/space variability structures in the climate system: The scale-dependent correlation analysis.” Clim. Dyn. 27: 441–458.
Scafetta, N. 2014. “Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO, and NAO indexes.” Clim. Dyn. 43 (1–2): 175–192. https://doi.org/10.1007/s00382-013-1771-3.
Torrence, C., and G. P. Compo. 1998. “A practical guide to wavelet analysis.” Bull. Am. Meteorol. Soc. 79 (1): 61–78. https://doi.org/10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2.
Torres, M. E., M. A. Colominas, G. Schlotthauer, and P. Fladrin. 2011. “A complete ensemble empirical mode decomposition with adaptive noise.” In Proc., IEEE Int. Conf. on Acoustic Speech and Signal Processing 22–27 May 2011, 4144–4147. Prague, Czechia.
Witten, I. H., E. Frank, and M. A. Hall. 2005. Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.
WRIS (Water Resources Information System). 2014. “Govt. of India: Watershed Atlas of India.” Accessed July 01, 2018. http://india-wris.nrsc.gov.in/Publications/WatershedSubbasinAtlas/2.10%20Mahanadi.pdf.
Wu, Z., and N. E. Huang. 2005a. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Calverton, MD: Center for Ocean-Land-Atmosphere Studies.
Wu, Z., and N. E. Huang. 2005b. “Statistical significance test of intrinsic mode functions.” In Hilbert Huang transform and its applications, edited by N. E. Huang, and S. S. P. Shen. Singapore: World Scientific Publishing.
Zhu, S., J. Zhou, L. Ye, and C. Meng. 2016. “Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China.” Environ. Earth Sci. 75 (6): 531. https://doi.org/10.1007/s12665-016-5337-7.
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Journal of Hydrologic Engineering
Volume 24 • Issue 1 • January 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 6, 2016
Accepted: Jul 24, 2018
Published online: Oct 31, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 31, 2019
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