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

• The paper reviews applications of hybrid wavelet–AI models in hydro-climatology.
• Efficiency of hybrid models regarding processes and model type were investigated.
• Survey shows wavelet pre-processor capability to enhance AI models performance.
• Organized information about wavelet–AI models can show the future research pass.

SummaryAccurate 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.