Linear genetic programming for time-series modelling of daily flow rate |
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| Authors: | Aytac Guven |
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| Institution: | (1) The Highest Prestige Scientific and Professional National Foundation, National Elites Foundation, Tehran, Iran;(2) College of Civil Engineering, Iran University of Science & Technology (IUST), Tehran, Iran;(3) College of Civil Engineering, Tafresh University, Tafresh, Iran |
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| 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. |
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