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Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

Abstract

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model’s efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month−1), root mean square error (1.14 BCM month−1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.

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Acknowledgements

This work was supported by a research grant from the Ministry of Higher Education, FRGS/1/2016/STG06/UKM/02/1, and a grant from the University of Malaya’s BKP Grant BK023-2015. The authors thank the Nile Water Authority (NWA) and Aswan High Dam Authority (AHDA), Ministry of Water Resources and Irrigation, Egypt for providing the natural inflow data sets used in this study. Dr. R C Deo also acknowledges the University of Southern Queensland s-ADOSP research grant (Sept – Nov 2017). The authors would like to thank the research facilities support received from DIP-2015-012 project funded by University Kebangsaan Malaysia for the sixth author.The authors thank all reviewers and the Editor-In-Chief for their insightful comments that has improved the quality of the final manuscript.

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Correspondence to Mohammed Falah Allawi.

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Allawi, M.F., Jaafar, O., Mohamad Hamzah, F. et al. Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor Appl Climatol 134, 545–563 (2018). https://doi.org/10.1007/s00704-017-2292-5

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