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Hybrid Wavelet Neural Network Approach

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

Application of Wavelet transformation (WT) has been found effective in dealing with the issue of non-stationary data. WT is a mathematical tool that improves the performance of Artificial Neural Network (ANN) models by simultaneously considering both the spectral and the temporal information contained in the input data. WT decomposes the main time series data into its sub-components. ANN models developed using input data processed by the WT instead of using data in its raw form are known as hybrid wavelet models. The hybrid wavelet data driven models, using multi-scale input data, results in improved performance by capturing useful information concealed in the main time series data in its raw form. This chapter will cover theoretical as well as practical applications of hybrid wavelet neural network models in hydrology.

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References

  1. T.J. Mulvany, On the use of self-registering rain and flood gauges, in Making Observations of the Relations of Rain Fall and Flood Discharges in a Given Catchment. Transactions and Minutes of the Proceedings of the Institute of Civil Engineers of Ireland, vol. 1 (Dublin, Ireland, Session, 1850)

    Google Scholar 

  2. S.J. Birkinshaw, SHETRAN Hydrological Model (2013), http://research.ncl.ac.uk/shetran/

  3. C. Downer, F.L. Ogden, GSSHA: A model for simulating diverse streamflow generation processes. J. Hydrol. Eng. 9(3), 161–174 (2004)

    Article  Google Scholar 

  4. M.N. French, W.F. Krajewski, R.R. Cuykendall, Rainfall forecasting in space and time using a neural network. J. Hydrol. 137(1), 1–31 (1992)

    Google Scholar 

  5. A.Y. Shamseldin, Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199(3–4), 272–294 (1997)

    Article  Google Scholar 

  6. M.A. Antar, I. Elassiouti, M.N. Allam, Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrol. Process. 20(5), 1201–1216 (2006)

    Article  Google Scholar 

  7. K. Aziz et al., Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch. Env. Res. Risk Assess. 28(3), 541–554 (2014)

    Article  Google Scholar 

  8. C.W. Dawson, R. Wilby, An artificial neural network approach to rainfall-runoff modelling. Hydrol. Sci. J. 43(1), 47–66 (1998)

    Article  Google Scholar 

  9. K.L. Hsu, H.V. Gupta, S. Sorooshian, Artificial neural network modeling of the rainfall‐runoff process. Water Resour. Res. 31(10), 2517–2530 (1995)

    Google Scholar 

  10. A. Jain, K.P. Sudheer, S. Srinivasulu, Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol. Process. 18(3), 571–581 (2004)

    Article  Google Scholar 

  11. V. Nourani, M.T. Alami, M.H. Aminfar, A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 22(3), 466–472 (2009)

    Article  Google Scholar 

  12. N. Sajikumar, B. Thandaveswara, A non-linear rainfall–runoff model using an artificial neural network. J. Hydrol. 216(1), 32–55 (1999)

    Article  Google Scholar 

  13. A.R. Senthil Kumar et al., Rainfall‐runoff modelling using artificial neural networks: comparison of network types. Hydrol. Process. 19(6), 1277–1291 (2005)

    Google Scholar 

  14. A.S. Tokar, P.A. Johnson, Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 4(3), 232–239 (1999)

    Article  Google Scholar 

  15. R.S. Govindaraju, Artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Eng. 5(2), 115–123 (2000)

    Article  Google Scholar 

  16. R.S. Govindaraju, Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Eng. 5(2), 124–137 (2000)

    Article  Google Scholar 

  17. R.J. Abrahart et al., Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog. Phys. Geogr. 36(4), 480–513 (2012)

    Article  Google Scholar 

  18. B. Cannas et al., Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Phys. Chem. Earth, Parts A/B/C 31(18), 1164–1171 (2006)

    Article  Google Scholar 

  19. G.P. Nason, R.V. Sachs, Wavelets in time-series analysis, vol. 357 (1999), pp, 2511–2526

    Google Scholar 

  20. V. Nourani et al., Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. J. Hydrol. 514, 358–377 (2014)

    Article  Google Scholar 

  21. D. Gabor, Theory of communications. Part 1:The analysis of information. J. Inst. Electr. Eng. 95(38), 429–441 (1948)

    Google Scholar 

  22. A. Grossmann, J. Morlet, Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15(4), 723–736 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  23. J. Adamowski, K. Sun, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 390(1), 85–91 (2010)

    Article  Google Scholar 

  24. R.M. Singh, Wavelet-ANN model for flood events, in Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20–22, 2011 (Springer, 2012)

    Google Scholar 

  25. G.S. Mallat, A theory for multiresolution signal decomposition: the wavelet representaiton. IEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  26. M.K. Tiwari, C. Chatterjee, Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach. J. Hydrol. 394(3–4), 458–470 (2010)

    Article  Google Scholar 

  27. M. Nakken, Wavelet analysis of rainfall–runoff variability isolating climatic from anthropogenic patterns. Environ. Model Softw. 14(4), 283–295 (1999)

    Article  Google Scholar 

  28. D. Labat, R. Ababou, A. Mangin, Rainfall–runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. J. Hydrol. 238(3), 149–178 (2000)

    Article  Google Scholar 

  29. D. Labat, R. Ababou, A. Mangin, Introduction of wavelet analyses to rainfall/runoffs relationship for a karstic basin: The case of Licq-Atherey karstic system (France). Groundwater 39(4), 605–615 (2001)

    Article  Google Scholar 

  30. W. Wang, J. Ding, Wavelet network model and its application to the prediction of hydrology. Nat. Sci 1(1), 67–71 (2003)

    MathSciNet  Google Scholar 

  31. F. Anctil, D.G. Tape, An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J. Environ. Eng. Sci. 3(S1), S121–S128 (2004)

    Article  Google Scholar 

  32. D. Mwale, T.Y. Gan, Wavelet analysis of variability, teleconnectivity, and predictability of the september–november east african rainfall. J. Appl. Meteorol. 44(2), 256–269 (2005)

    Article  Google Scholar 

  33. D. Mwale et al., Wavelet empirical orthogonal functions of space-time-frequency regimes and predictability of southern Africa summer rainfall. J. Hydrol. Eng. 12(5), 513–523 (2007)

    Article  Google Scholar 

  34. J.F. Adamowski, Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol. 353(3), 247–266 (2008)

    Article  Google Scholar 

  35. J.F. Adamowski, River flow forecasting using wavelet and cross-wavelet transform models. Hydrol. Process. 22(25), 4877–4891 (2008)

    Article  Google Scholar 

  36. C.-C. Kuo, T.Y. Gan, P.-S. Yu, Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan. Mon. Weather Rev. 138(1), 162–175 (2010)

    Article  Google Scholar 

  37. C.-C. Kuo, T.Y. Gan, P.-S. Yu, Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan. J. Hydrol. 387(3), 292–303 (2010)

    Article  Google Scholar 

  38. M. Özger, Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng. 37(16), 1443–1451 (2010)

    Article  Google Scholar 

  39. T. Partal, Ö. Kişi, Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J. Hydrol. 342(1–2), 199–212 (2007)

    Article  Google Scholar 

  40. T. Partal, H.K. Cigizoglu, Prediction of daily precipitation using wavelet—neural networks. Hydrol. Sci. J. 54(2), 234–246 (2009)

    Article  Google Scholar 

  41. O. Kisi, J. Shiri, Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour. Manage. 25(13), 3135–3152 (2011)

    Article  Google Scholar 

  42. R.V. Ramana et al., Monthly rainfall prediction using wavelet neural network analysis. Water Resour. Manage. 27(10), 3697–3711 (2013)

    Article  Google Scholar 

  43. W. Wang, J. Jin, Y. Li, Prediction of inflow at three gorges dam in Yangtze River with wavelet network model. Water Resour. Manage. 23(13), 2791–2803 (2009)

    Article  Google Scholar 

  44. H.-C. Zhou, Y. Peng, G.-H. Liang, The research of monthly discharge predictor-corrector model based on wavelet decomposition. Water Resour. Manage. 22(2), 217–227 (2008)

    Article  Google Scholar 

  45. Ö. Kişi, Stream flow forecasting using neuro-wavelet technique. Hydrol. Process. 22(20), 4142–4152 (2008)

    Article  Google Scholar 

  46. Ö. Kişi, Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J. Hydrol. Eng. 14(8), 773–782 (2009)

    Article  Google Scholar 

  47. T. Partal, River flow forecasting using different artificial neural network algorithms and wavelet transform. Can. J. Civ. Eng. 36(1), 26–38 (2008)

    Article  Google Scholar 

  48. J. Adamowski, K. Sun, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 390(1–2), 85–91 (2010)

    Article  Google Scholar 

  49. N. Pramanik, R. Panda, A. Singh, Daily river flow forecasting using wavelet ANN hybrid models. J. Hydroinformatics 13(1), 49–63 (2011)

    Article  Google Scholar 

  50. J. Shiri, O. Kisi, Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J. Hydrol. 394(3), 486–493 (2010)

    Article  Google Scholar 

  51. Y. Wang et al., Flood simulation using parallel genetic algorithm integrated wavelet neural networks. Neurocomputing 74(17), 2734–2744 (2011)

    Article  Google Scholar 

  52. R. Maheswaran, R. Khosa, Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River. J. Hydroinformatics 15(3), 1022–1041 (2013)

    Article  Google Scholar 

  53. M. Shoaib, A.Y. Shamseldin, B.W. Melville, Comparative study of different wavelet based neural network models for rainfall-runoff modeling. J. Hydrol. 515, 47–58 (2014)

    Article  Google Scholar 

  54. V. Nourani, Ö. Kisi, M. Komasi, Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. J. Hydrol. 402(1), 41–59 (2011)

    Article  Google Scholar 

  55. R. Maheswaran, R. Khosa, Wavelet-Volterra coupled model for monthly stream flow forecasting. J. Hydrol. 450–451, 320–335 (2012)

    Article  Google Scholar 

  56. A. Aussem, J. Campbell, F. Murtagh, Wavelet-based feature extraction and decomposition strategies for financial forecasting. J. Comput. Intell. Finan. 6(2), 5–12 (1998)

    Google Scholar 

  57. V. Nourani, M. Komasi, A. Mano, A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour. Manage. 23(14), 2877–2894 (2009)

    Article  Google Scholar 

  58. M. Shoaib et al., Hybrid wavelet neuro-fuzzy approach for rainfall-runoff modeling. J. Comput. Civil Eng. (2014)

    Google Scholar 

  59. M. Shoaib et al., Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach. J. Hydrol. 527, 326–344 (2015)

    Article  Google Scholar 

  60. C.W. Dawson et al., Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China. Hydrol. Earth Syst. Sci. Dis. 6(4), 619–626 (2002)

    Article  Google Scholar 

  61. Y.B. Dibike, D. Solomatine, M.B. Abbott, On the encapsulation of numerical-hydraulic models in artificial neural network. J. Hydraul. Res. 37(2), 147–161 (1999)

    Article  Google Scholar 

  62. A. El-Shafie et al., Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int. J. Phys. Sci. 6(8), 1997–2003 (2011)

    Google Scholar 

  63. A. Jain, A.M. Kumar, Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput. 7(2), 585–592 (2007)

    Article  MathSciNet  Google Scholar 

  64. A.W. Minns, M.J. Hall, Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 41(3), 399–417 (1996)

    Article  Google Scholar 

  65. R. Modarres, Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrol. Earth Syst. Sci. 13(3), 411–421 (2009)

    Article  Google Scholar 

  66. P. Phukoetphim, A.Y. Shamseldin, B.W. Melville, Knowledge extraction from artificial neural network for rainfall-runoff models combination system. J. Hydrol. Eng. (2013)

    Google Scholar 

  67. S. Riad et al., Predicting catchment flow in a semi-arid region via an artificial neural network technique. Hydrol. Process. 18(13), 2387–2393 (2004)

    Article  Google Scholar 

  68. S. Srinivasulu, A. Jain, A comparative analysis of training methods for artificial neural network rainfall–runoff models. Appl. Soft Comput. 6(3), 295–306 (2006)

    Article  Google Scholar 

  69. Z. Waszczyszyn, Fundamentals of Artificial Neural Networks (Springer, 1999)

    Google Scholar 

  70. M. Motter, J.C. Principe. A gamma memory neural network for system identification. in Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on. 1994. IEEE

    Google Scholar 

  71. C.H. Van Iddekinge, R.E. Ployhart, Developments in the criterion-related validation of selection procedures: a critical review and recommendations for practice. Pers. Psychol. 61(4), 871–925 (2008)

    Article  Google Scholar 

  72. M. Anthony, P.L. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, 2009)

    Google Scholar 

  73. K. Aziza et al., Co-Active Neuro Fuzzy Inference System for Regional Flood Estimation in Australia. (Editorial Board, 2013), p. 11

    Google Scholar 

  74. C.-T. Cheng et al., Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models, in Advances in Natural Computation. (Springer, 2005), pp. 1152–1161

    Google Scholar 

  75. A.P. Jacquin, A.Y. Shamseldin, Development of rainfall–runoff models using Takagi-Sugeno fuzzy inference systems. J. Hydrol. 329(1), 154–173 (2006)

    Article  Google Scholar 

  76. A. Lohani, R. Kumar, R. Singh, Hydrological Time Series Modeling: A Comparison Between Adaptive Neuro Fuzzy, Neural Network And Auto Regressive Techniques (Journal of Hydrology, 2012)

    Google Scholar 

  77. P.C. Nayak et al., A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol. 291(1–2), 52–66 (2004)

    Article  Google Scholar 

  78. P.C. Nayak, K.P. Sudheer, K.S. Ramasastri, Fuzzy computing based rainfall–runoff model for real time flood forecasting. Hydrol. Process. 19(4), 955–968 (2005)

    Article  Google Scholar 

  79. Nayak, P.C., et al., Short‐term flood forecasting with a Neurofuzzy model. Water Resour. Res. 41(4) (2005)

    Google Scholar 

  80. P.C. Nayak, K.P. Sudheer, S.K. Jain, Rainfall‐runoff modeling through hybrid intelligent system. Water Resour. Res. 43(7) (2007)

    Google Scholar 

  81. A. Talei, L.H.C. Chua, C. Quek, A novel application of a neuro-fuzzy computational technique in event-based rainfall–runoff modeling. Expert Syst. Appl. 37(12), 7456–7468 (2010)

    Article  Google Scholar 

  82. B. Zhang, R.S. Govindaraju, Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour. Res. 36(3), 753–762 (2000)

    Article  Google Scholar 

  83. M.P. Rajurkar, U.C. Kothyari, U.C. Chaube, Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 285(1), 96–113 (2004)

    Article  Google Scholar 

  84. Koza, J.R., Genetic Programming: On the programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992)

    Google Scholar 

  85. A. Aytek, M. Alp, An application of artificial intelligence for rainfall-runoff modeling. J. Earth Syst. Sci. 117(2), 145–155 (2008)

    Article  Google Scholar 

  86. V. Babovic, M.B. Abbott, The evolution of equations from hydraulic data Part I: Theory. J. Hydraul. Res. 35(3), 397–410 (1997)

    Article  Google Scholar 

  87. V. Babovic, M.B. Abbott, The evolution of equations from hydraulic data Part II: Applications. J. Hydraul. Res. 35(3), 411–430 (1997)

    Article  Google Scholar 

  88. Drecourt, J.-P., Application of neural networks and genetic programming to rainfall-runoff modeling. D2 K Technical Rep, 1999(0699-1): p. 1

    Google Scholar 

  89. L.-C. Chang, C.-C. Ho, Y.-W. Chen, Applying multiobjective genetic algorithm to analyze the conflict among different water use sectors during drought period. J. Water Resour. Plann. Manage. 136(5), 539–546 (2009)

    Article  Google Scholar 

  90. S.T. Khu et al., Genetic programming and its application in real-time runoff forecasting1. JAWRA J. Am. Water Resour. Assoc. 37(2), 439–451 (2001)

    Article  MathSciNet  Google Scholar 

  91. T. Rajaee, Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Sci. Total Environ. 409(15), 2917–2928 (2011)

    Article  Google Scholar 

  92. H.M. Azamathulla et al., Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water Resour. Manage 25(11), 2901–2916 (2011)

    Article  Google Scholar 

  93. A. Guven, A. Aytek, New approach for stage–discharge relationship: gene-expression programming. J. Hydrol. Eng. 14(8), 812–820 (2009)

    Article  Google Scholar 

  94. O. Kisi, J. Shiri, B. Nikoofar, Forecasting daily lake levels using artificial intelligence approaches. Comput. Geosci. 41, 169–180 (2012)

    Article  Google Scholar 

  95. O. Kisi, J. Shiri, M. Tombul, Modeling rainfall-runoff process using soft computing techniques. Comput. Geosci. 51, 108–117 (2013)

    Article  Google Scholar 

  96. J. Shiri et al., Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J. Hydrol. 414, 302–316 (2012)

    Article  Google Scholar 

  97. O. Kisi, Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour. Manage. 25(2), 579–600 (2011)

    Article  Google Scholar 

  98. R. Maheswaran, R. Khosa, Comparative study of different wavelets for hydrologic forecasting. Comput. Geosci. 46, 284–295 (2012)

    Article  Google Scholar 

  99. V. Nourani, M. Komasi, M.T. Alami, Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff Process. J. Hydrol. Eng. 17(6), 724–741 (2011)

    Article  Google Scholar 

  100. V. Nourani et al., Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J. Hydrol. 476, 228–243 (2013)

    Article  Google Scholar 

  101. N. Vahid, K. TohidRezapour, B. AidaHosseini, Implication of Feature Extraction Methods to Improve Performance of Hybrid Wavelet-ANN Rainfall?Runoff Model, in Case Studies in Intelligent Computing (Auerbach Publications, 2014), pp. 457–498

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The University of Auckland, Private Bag 92019, Auckland, New Zealand

    Muhammad Shoaib, Asaad Y. Shamseldin & Bruce W. Melville

  2. Department of Civil Engineering, Bahauddin Zakariya University, Multan, Pakistan

    Mudasser Muneer Khan

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  1. Muhammad Shoaib
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  2. Asaad Y. Shamseldin
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  3. Bruce W. Melville
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  4. Mudasser Muneer Khan
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Correspondence to Muhammad Shoaib .

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  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M. (2016). Hybrid Wavelet Neural Network Approach. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_7

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