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Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins

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Abstract

Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Qt–1) is the most crucial variable for daily runoff prediction.

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Data availability

Some or all data that support this study’s findings are available from the corresponding author upon reasonable request.

Code availability

The models or codes used to develop this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this manuscript further.

Funding

Alban Kuriqi was supported by a Ph.D. scholarship granted by Fundação para a Ciência e a Tecnologia, I.P.P (FCT), Portugal, and the Ph.D. Program FLUVIO–River Restoration and Management, grant number: PD/BD/114558/2016.

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Authors and Affiliations

  1. Department of Soil Science and Agriculture Chemistry, School of Agriculture, Lovely Professional University, Phagwara, 144411, India

    Tarate Suryakant Bajirao

  2. Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, 263145, India

    Pravendra Kumar & Manish Kumar

  3. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  4. CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, 1049-001, Portugal

    Alban Kuriqi

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  1. Tarate Suryakant Bajirao
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Contributions

Conceptualization: Tarate Suryakant Bajirao and Pravendra Kumar; methodology: Tarate Suryakant Bajirao, Pravendra Kumar, and Manish Kumar; formal analysis and investigation: Tarate Suryakant Bajirao, Pravendra Kumar, Ahmed Elbeltagi; writing—original draft preparation: Tarate Suryakant Bajirao; writing—review and editing: Tarate Suryakant Bajirao, Pravendra Kumar, Manish Kumar, Ahmed Elbeltagi, and Alban Kuriqi; resources: Pravendra Kumar; supervision: Alban Kuriqi.

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Correspondence to Alban Kuriqi.

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Bajirao, T.S., Kumar, P., Kumar, M. et al. Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theor Appl Climatol 145, 1207–1231 (2021). https://doi.org/10.1007/s00704-021-03681-2

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