180
Views
3
CrossRef citations to date
0
Altmetric
Research Papers

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

&
Pages 1-8 | Received 27 Jan 2016, Accepted 14 Jun 2016, Published online: 15 Jul 2016

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Fugal, L.D., 2009. Conceptual wavelets in digital signal processing. San Diego: Space and Signals Technical Publishing.
  • Garbrecht, J.D., 2006. Comparison of three alternative ANN designs for monthly rainfall-runoff simulation. Journal of Hydrological Engineering, 11 (5), 502–505.
  • 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.
  • Golyandina, N., Nekrutkin, V., and Zhigljavsky, A., 2001. Analysis of time series structure: SSA and related techniques. New York: Chapman & Hall/CRC, 305.
  • Hassani, H., 2007. Singular spectrum analysis: methodology and comparison. Journal of Data Science, 5 (2), 239–257.
  • 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
  • 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
  • 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
  • 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
  • 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
  • MacLeod, C., 1999. The synthesis of artificial neural networks using single string evolutionary techniques. (PhD Dissertation). The Robert Gordon University, Aberdeen, Scotland.
  • 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
  • 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
  • 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)
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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)
  • Vanfleet, P.J., 2008. Discrete wavelet transformation, an elementary approach with applications. Hoboken, NJ: Wiley Interscience.
  • Wang, W. and Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1 (1), 67–71.
  • 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
  • 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
  • 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
  • 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
  • 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

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.