• Linear genetic programming for time-series modelling of daily flow rate

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      Permanent link:
      https://www.ias.ac.in/article/fulltext/jess/118/02/0137-0146

    • Keywords

       

      Genetic programming; neural networks; daily flows; flow forecasting.

    • Abstract

       

      In this study linear genetic programming (LGP),which is a variant of Genetic Programming,and two versions of Neural Networks (NNs)are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne,PA,USA.Daily flow rate at present is being predicted based on different time-series scenarios.For this purpose,various LGP and NN models are calibrated with training sets and validated by testing sets.Additionally,the robustness of the proposed LGP and NN models are evaluated by application data,which are used neither in training nor at testing stage.The results showed that both techniques predicted the flow rate data in quite good agreement with the observed ones,and the predictions of LGP and NN are challenging.The performance of LGP,which was moderately better than NN,is very promising and hence supports the use of LGP in predicting of river flow data.

    • Author Affiliations

       

      Aytac Guven1

      1. Civil Engineering Department, Gaziantep University, 27310 Gaziantep, Turkey.
    • Dates

       
  • Journal of Earth System Science | News

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