Abstract
The modelling of the rainfall–runoff relationship plays an important role in risk reduction and prevention against water-related disasters and in water resources management. In this research, we have modelled the rainfall–runoff relationship using intelligent hybrid models for forecasting daily flow rates of the Sebaou basin located in northern Algeria. As such, two hybrid approaches of artificial intelligence have been used in this study. These approaches are based on the adaptive neuro-fuzzy inference system combined with hydrological signal decomposition techniques. The first is derived from the Hilbert–Huang transform called the empirical mode decomposition and the other is derived from the discrete wavelet transform called multiresolution analysis. The results obtained seem to be very encouraging and the techniques appear promising. The performances of the hybrid models are relatively much higher than the other models used for comparison in this study. Although the technique of parallel computing has been used and despite the power of the computing station, the relatively long computation time is the main disadvantage of these models.
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Acknowledgements
We would like to thank the National Agency of Hydraulic Resources for providing the hydrological data and the Directorate General for Scientific Research and Technological Development for supporting this research project.
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Abda, Z., Chettih, M. Forecasting daily flow rate-based intelligent hybrid models combining wavelet and Hilbert–Huang transforms in the mediterranean basin in northern Algeria. Acta Geophys. 66, 1131–1150 (2018). https://doi.org/10.1007/s11600-018-0188-0
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DOI: https://doi.org/10.1007/s11600-018-0188-0