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Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model

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Abstract

A precise forecast of streamflow in intermittent rivers is of major difficulties and challenges in watershed management, particularly in arid and semiarid regions. The present research study introduces an ensemble gene expression programming (EGEP) modeling approach to 1- and 2-day ahead streamflow forecasts that meet both accuracy and simplicity criteria of an applied model. Three main components of the proposed EGEP approach which are capable of producing a parsimonious model include (i) creating a population of suitable solutions using classic genetic programming (GP) instead of a single solution, (ii) combining the solutions throughout gene expression programming, and (iii) parsimony selection based upon trade-off analysis between the complexity and accuracy of the best-evolved solutions at the holdout validation set. The EGEP model was trained and verified using the streamflow measurements from the Shahrchay River lying northwest of Iran. Several statistical indicators were computed for verification of the ensemble models’ accuracy with that of classic GP and artificial neural network models developed as the benchmarks. Our results revealed that the EGEP outperforms the benchmarks. It is an explicit, simple, and precise approach and, therefore, worthy to be used in practice.

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Acknowledgments

The authors thank Western Azerbaijan Regional Water Authority (agrw.ir) for providing streamflow data used in this research study. Insightful comments from the three anonymous reviewers are gratefully appreciated.

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Authors and Affiliations

  1. Department of Civil Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran

    Aidin Rahmani-Rezaeieh & Mirali Mohammadi

  2. Department of Civil Engineering, Faculty of Engineering, Urmia University, P.O. Box 165, Urmia, Iran

    Mirali Mohammadi

  3. Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey

    Ali Danandeh Mehr

Authors
  1. Aidin Rahmani-Rezaeieh
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  2. Mirali Mohammadi
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  3. Ali Danandeh Mehr
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Correspondence to Mirali Mohammadi.

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Appendix

Appendix

Dimensionless gene expression trees of the parsimonious EGEP models developed for daily streamflow prediction in Shahrchay River

Fig. 14
figure 14

The parsimonious EGEP tree developed for 1-day ahead forecast of daily streamflow in Shahrchay River

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Fig. 15
figure 15

Same as Fig. 12, but for 2-day ahead forecasts

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Rahmani-Rezaeieh, A., Mohammadi, M. & Danandeh Mehr, A. Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model. Theor Appl Climatol 139, 549–564 (2020). https://doi.org/10.1007/s00704-019-02982-x

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