Applications of hybrid wavelet-Artificial Intelligence models in hydrology: A review

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  author =       "Vahid Nourani and Aida Hosseini Baghanam and 
                 Jan Adamowski and Ozgur Kisi",
  title =        "Applications of hybrid wavelet-Artificial Intelligence
                 models in hydrology: A review",
  journal =      "Journal of Hydrology",
  year =         "2014",
  volume =       "514",
  pages =        "358--377",
  month =        "6 " # jun,
  keywords =     "genetic algorithms, genetic programming,
                 Hydro-climatology, Black box model, Artificial
                 Intelligence, Wavelet transform, Hybrid model",
  ISSN =         "0022-1694",
  URL =          "",
  DOI =          "doi:10.1016/j.jhydrol.2014.03.057",
  size =         "20 pages",
  abstract =     "Accurate and reliable water resources planning and
                 management to ensure sustainable use of watershed
                 resources cannot be achieved without precise and
                 reliable models. Notwithstanding the highly stochastic
                 nature of hydrological processes, the development of
                 models capable of describing such complex phenomena is
                 a growing area of research. Providing insight into the
                 modeling of complex phenomena through a thorough
                 overview of the literature, current research, and
                 expanding research horizons can enhance the potential
                 for accurate and well designed models.

                 The last couple of decades have seen remarkable
                 progress in the ability to develop accurate hydrologic
                 models. Among various conceptual and black box models
                 developed over this period, hybrid wavelet and
                 Artificial Intelligence (AI)-based models have been
                 amongst the most promising in simulating hydrologic
                 processes. The present review focuses on defining
                 hybrid modelling, the advantages of such combined
                 models, as well as the history and potential future of
                 their application in hydrology to predict important
                 processes of the hydrologic cycle. Over the years, the
                 use of wavelet AI models in hydrology has steadily
                 increased and attracted interest given the robustness
                 and accuracy of the approach. This is attributable to
                 the usefulness of wavelet transforms in
                 multi-resolution analysis, de-noising, and edge effect
                 detection over a signal, as well as the strong
                 capability of AI methods in optimisation and prediction
                 of processes. Several ideas for future areas of
                 research are also presented in this paper.",
  notes =        "Errata page 368, column 1, line 41, the phrase
                 'provided originally by' should be changed to 'as well
                 as'. Vahid Nourani, Aida H. Baghanam, Jan Adamowski,
                 Ozgur Kisi Corrigendum to Applications of hybrid
                 wavelet Artificial Intelligence models in hydrology: A
                 review [J. Hydrol. 514 (2014) 358-377] Journal of
                 Hydrology, Volume 517, 19 September 2014, Page 1189

Genetic Programming entries for Vahid Nourani Aida Hosseini Baghanam Jan Adamowski Ozgur Kisi