- 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 = "
http://www.sciencedirect.com/science/article/pii/S002216941400242X",
-
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 DOI:10.1016/j.jhydrol.2014.06.024 http://www.sciencedirect.com/science/article/pii/S0022169414004843",
