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A hybrid support vector regression–firefly model for monthly rainfall forecasting

  • A. Danandeh MehrEmail author
  • V. Nourani
  • V. Karimi Khosrowshahi
  • M. A. Ghorbani
Original Paper

Abstract

Long-term prediction of rainfalls is one of the most challenging tasks in stochastic hydrology owing to the highly random characteristics of rainfall events. In this paper, a novel approach is adopted to develop a hybrid regression model for 1-month-ahead rainfall forecasting at two rain gauge locations (namely: Tabriz and Urmia stations), in northwest Iran. The approach is based on the integration of support vector regression (SVR) and firefly algorithm (FFA) that results in truthful rainfall forecasts. The proposed hybrid model was trained and validated using weak stationary state of monthly rainfall data obtained from the gauges. The efficiency results of the model were also cross-validated with those of stand-alone SVR- and genetic programming-based forecasting models developed as the benchmarks in this study. For both rain gauge locations, the results showed that the hybrid model significantly outperforms the benchmarks. With respect to the average efficiency results at the gauge locations, the FFA-induced improvement in the SVR forecasts was matched by an approximately 30% decrease in root-mean-square error and around 100% increase in Nash–Sutcliffe efficiency. Such a promising accuracy in the proposed model may recommend its application at monthly rainfall forecasting in the present semiarid region.

Keywords

Support vector regression Rainfall Time series modeling Firefly algorithm Multigene genetic programming Iran 

Notes

Acknowledgements

This research was partly supported by funding from Iran’s National Elites Foundation (BMN). The authors gratefully acknowledge Technology Affairs of University of Tabriz for their tremendous help during the research. The authors also thank two anonymous reviewers for their fruitful comments on the initial version of the paper.

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Copyright information

© Islamic Azad University (IAU) 2018

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of EngineeringAntalya Bilim UniversityAntalyaTurkey
  2. 2.Department of Water Resources Engineering, Faculty of Civil EngineeringUniversity of TabrizTabrizIran
  3. 3.Department of Civil Engineering, Faculty of EngineeringNear East UniversityNicosiaTurkey
  4. 4.Department of Water EngineeringUniversity of TabrizTabrizIran

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