Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model

References

• Abdellatif, M.E., Osman, Y.Z., and Elkhidir, A.M., 2015. Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan. International Journal of River Basin Management, 13 (2), 203–214. doi: 10.1080/15715124.2014.1003381
   Web of Science ®Google Scholar
• Abrahart, R.J. and See, L., 2002. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrology and Earth System Sciences, 6 (4), 655–670. doi:10.5194/hess-6-655-2002
   Web of Science ®Google Scholar
• Anctil, F. and Lauzon, N., 2004. Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions. Hydrology and Earth System Sciences, 8 (5), 940–958. doi: 10.5194/hess-8-940-2004
   Web of Science ®Google Scholar
• Anctil, F., et al., 2004. A soil moisture index as an auxiliary ANN input for stream flow forecasting. Journal of Hydrology, 286, 155–167. doi:10.1016/j.jhydrol.2003.09.006
   Web of Science ®Google Scholar
• Baratta, D., et al., 2003. Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Networks, 16 (3–4), 375–387. doi:10.1016/S0893-6080(03)00022-4
   PubMed Web of Science ®Google Scholar
• Boughton, W.C. and Droop, O.P., 2003. Continuous simulation for design flood estimation – a review. Environmental Modelling and Software, 18 (4), 309–318. doi:10.1016/S1364-8152(03)00004-5
   Web of Science ®Google Scholar
• Broomhead, D.S. and King, G.P., 1986a. Extracting qualitative dynamics from experimental data. Physica D, 20, 217–236. doi: 10.1016/0167-2789(86)90031-X
   Web of Science ®Google Scholar
• Broomhead, D.S. and King, G.P., 1986b. On the qualitative analysis of experimental dynamical systems. In: S. Sarkar, ed. Nonlinear phenomena and chaos. Bristol: Adam Hilger, 113–144.
   Google Scholar
• Burger, C.M., et al., 2007. Future climate scenarios and rainfall runoff modelling in the Upper Gallego catchment (Spain). Environmental Pollution, 148, e842–e854. doi:10.1016/j.envpol.2007.02.002
   PubMed Web of Science ®Google Scholar
• Chen, X.Y., Chau, K.W., and Busari, A.O., 2015a. A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence, 46 (A), 258–268. doi: 10.1016/j.engappai.2015.09.010
   Web of Science ®Google Scholar
• Chen, D., et al., 2015b. Deriving optimal daily reservoir operation scheme with consideration of downstream ecological hydrograph through a time-nested approach. Water Resources Management, 29 (9), 3371–3386. doi:10.1007/s11269-015-1005-z
   Web of Science ®Google Scholar
• Corzo, G.A., et al., 2009. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin. Hydrology and Earth System Sciences, 13, 1619–1634. doi:10.5194/hess-13-1619-2009
   Web of Science ®Google Scholar
• Dariane, A.B. and Karami, F., 2014. Deriving hedging rules of multi-reservoir system by online evolving neural networks. Water Resources Management, 28 (11), 3651–3665. doi:10.1007/s11269-014-0693-0
   Web of Science ®Google Scholar
• Dariane, A.B. and Moradi, M., 2014. A comparative analysis of evolving artificial neural network and reinforcement learning in stochastic optimization of multireservoir systems. Hydrological Sciences Journal. doi:10.1080/02626667.2014.986485
   Web of Science ®Google Scholar
• Dariane, A.B. and Azimi, Sh., 2014. Forecasting streamflow by combination of genetic input selection algorithm and wavelet transform using ANFIS model. Hydrological Sciences Journal. doi:10.1080/02626667.2014.988155
   Web of Science ®Google Scholar
• Fugal, L.D., 2009. Conceptual wavelets in digital signal processing. San Diego: Space and Signals Technical Publishing.
   Google Scholar
• Garbrecht, J.D., 2006. Comparison of three alternative ANN designs for monthly rainfall-runoff simulation. Journal of Hydrological Engineering, 11 (5), 502–505.
   Google Scholar
• Gautam, M.R., Watanabe, K., and Saegusa, H., 2000. Runoff analysis in humid forest catchment with artificial neural network. Journal of Hydrology, 235 (1-2), 117–136.
   Google Scholar
• Golyandina, N., Nekrutkin, V., and Zhigljavsky, A., 2001. Analysis of time series structure: SSA and related techniques. New York: Chapman & Hall/CRC, 305.
   Google Scholar
• Hassani, H., 2007. Singular spectrum analysis: methodology and comparison. Journal of Data Science, 5 (2), 239–257.
   Google Scholar
• Hassani, H. and Zhigljavsky, A., 2009. Singular spectrum analysis: methodology and application to economics data. Journal of Systems Science and Complexity, 22 (3), 372–394. doi:10.1007/s11424-009-9171-9
   Web of Science ®Google Scholar
• Hassani, H. and Thomakos, D., 2010. A review on singular spectrum analysis for economic and financial time series. Statistics and Its Interface, 3, 377–397. ISSN: 1938-7989 doi: 10.4310/SII.2010.v3.n3.a11
   Web of Science ®Google Scholar
• Hassani, H. and Mahmoudvand, R., 2013. Multivariate singular spectrum analysis: a general view and new vector forecasting approach. International Journal of Energy and Statistics, 1, 55–83. doi: 10.1142/S2335680413500051
   Google Scholar
• Krause, P., Boyle, D.P., and Base, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89–97. doi: 10.5194/adgeo-5-89-2005
   Google Scholar
• Labat, D., 2005. Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology, 314, 275–288. doi:10.1016/j.jhydrol.2005.04.003
   Web of Science ®Google Scholar
• MacLeod, C., 1999. The synthesis of artificial neural networks using single string evolutionary techniques. (PhD Dissertation). The Robert Gordon University, Aberdeen, Scotland.
   Google Scholar
• Marqus, C.A.F., et al., 2006. Singular spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth, 31, 1172–1179. doi:10.1016/j.pce.2006.02.061
   Web of Science ®Google Scholar
• Nilsson, P., Uvo, C.B., and Berndtsson, R., 2006. Monthly runoff simulation: comparing and combining conceptual and neural network models. Journal of Hydrology, 321, 344–363. doi:10.1016/j.jhydrol.2005.08.007
   Web of Science ®Google Scholar
• Olsson, J., et al., 2004. Neural networks for rain falling forecasting by atmospheric downscaling. Journal of Hydrologic Engineering, 9 (1), 1–12. doi: 10.1061/(ASCE)1084-0699(2004)9:1(1)
   Web of Science ®Google Scholar
• Partal, T. and Kisi, O., 2007. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342, 199–212. doi:10.2166/nh.2011.048
   Web of Science ®Google Scholar
• Penman, H.L., 1961. Weather, plant and soil factors in hydrology. Weather, 16 (7), 207–219. doi:10.1002/j.1477-8696.1961.tb01934.x
   Google Scholar
• Rajurkar, M.P., Kothyari, U.C., and Chaube, U.C., 2004. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285, 96–113. doi:10.1016/j.jhydrol.2003.08.011
   Web of Science ®Google Scholar
• Shoaib, M., et al., 2015. Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach. Journal of Hydrology, 527, 326–344. doi:10.1016/j.jhydrol.2015.04.072
   Web of Science ®Google Scholar
• Shrestha, D.L. and Solomatine, D.P., 2008. Data-driven approaches for estimating uncertainty in rainfall-runoff modeling. International Journal of River Basin Management, 6 (2), 109–122. doi: 10.1080/15715124.2008.9635341
   Google Scholar
• Sivapragasam, C., Liong, S.Y., and Pasha, M.F.K., 2001. Rainfall and runoff forecasting with SSA–SVM approach. Journal of Hydroinformatics, 3 (3), 141–152.
   Google Scholar
• Taormina, R. and Chau, K.W., 2015. Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529 (3), 1617–1632. doi: 10.1016/j.jhydrol.2015.08.022
   Web of Science ®Google Scholar
• Tokar, A.S. and Markus, M., 2000. Precipitation- runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering, 5 (2), 156–161. doi: 10.1061/(ASCE)1084-0699(2000)5:2(156)
   Web of Science ®Google Scholar
• Vanfleet, P.J., 2008. Discrete wavelet transformation, an elementary approach with applications. Hoboken, NJ: Wiley Interscience.
   Google Scholar
• Wang, W. and Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1 (1), 67–71.
   Google Scholar
• Wang, W.C., et al., 2015. Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management, 29 (8), 2655–2675. doi: 10.1007/s11269-015-0962-6
   Web of Science ®Google Scholar
• Wu, C.L., Chau, K.W., and Li, Y.S., 2009. Methods to improve neural network performance in daily flow prediction. Journal of Hydrology, 372 (1–4), 80–93. doi: 10.1016/j.jhydrol.2009.03.038
   Web of Science ®Google Scholar
• Wu, C.L., Chau, K.W., and Fan, C., 2010. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology, 389, 146–167. doi:10.1016/j.jhydrol.2010.05.040
   Web of Science ®Google Scholar
• Wu, C.L. and Chau, K.W., 2011. Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology, 399, 394–409. doi:10.1016/j.jhydrol.2011.01.017
   Web of Science ®Google Scholar
• Yiou, P., Sornette, D., and Ghil, M., 2000. Data-adaptive wavelets and multi-scale singular-spectrum analysis. Physica D, 142, 254–290. doi: 10.1016/S0167-2789(00)00045-2
   Web of Science ®Google Scholar