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

被引:559
作者
Nourani, Vahid [1 ]
Baghanam, Aida Hosseini [1 ]
Adamowski, Jan [2 ]
Kisi, Ozgur [3 ]
机构
[1] Univ Tabriz, Fac Civil Engn, Dept Water Resources Engn, Tabriz, Iran
[2] McGill Univ, Dept Bioresource Engn, Montreal, PQ H3A 2T5, Canada
[3] Canik Basari Univ, Fac Engn, Samsun, Turkey
关键词
Hydro-climatology; Black box model; Artificial Intelligence; Wavelet transform; Hybrid model; SUSPENDED SEDIMENT CONCENTRATION; FUZZY CONJUNCTION MODEL; SUPPORT VECTOR MACHINES; EMPIRICAL ORTHOGONAL FUNCTIONS; GENETIC PROGRAMMING APPROACH; NEURAL-NETWORK; TIME-SERIES; DATA-DRIVEN; GROUNDWATER LEVELS; MULTIRESOLUTION ANALYSIS;
D O I
10.1016/j.jhydrol.2014.03.057
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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 modeling, 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 optimization and prediction of processes. Several ideas for future areas of research are also presented in this paper. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:358 / 377
页数:20
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