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Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows

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Abstract

Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42 years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values ​​were selected as inputs according to partial autocorrelation values ​​in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.

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Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Thanks to the General Directorate of State Hydraulic Works for providing the streamflow data.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering Architecture, Erzincan Binali Yıldırım University, Erzincan, Turkey

    Okan Mert Katipoğlu, Sefa Nur Yeşilyurt & Hüseyin Yıldırım Dalkılıç

  2. Department of Computer Engineering, Faculty of Engineering Architecture, Erzincan Binali Yıldırım University, Erzincan, Turkey

    Funda Akar

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  1. Okan Mert Katipoğlu
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Contributions

O. M. Katipoğlu contributed to the data analysis, writing the introduction, results, and conclusions. S. N. Yeşilyurt contributed to the data analysis, writing methods, and results. H. Y. Dalkılıç contributed with data collection and reviewing. F. Akar contributed to data analysis and reviewing. All authors read and approved the final manuscript.

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Correspondence to Okan Mert Katipoğlu.

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Katipoğlu, O.M., Yeşilyurt, S.N., Dalkılıç, H.Y. et al. Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows. Environ Monit Assess 195, 1108 (2023). https://doi.org/10.1007/s10661-023-11700-0

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