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

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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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References

  • Akaike H 1974 A new look at the statistical model identification; IEEE Transactions on Automatic Control 19(6) 716–723.

    Article  Google Scholar 

  • Aytek A, Guven A, Yuce M İ and Aksoy H 2008 An explicit neural network formulation for evapotranspiration; Hydrol. Sci. 53(4) 893–904.

    Article  Google Scholar 

  • Aytek A and Alp M 2008 An application of artificial intelligence for rainfall-runoff modelling; J. Earth Syst. Sci. 117(2) 145–155.

    Article  Google Scholar 

  • Banzhaf W, Nordin P, Keller R and Francone F 1998 Genetic Programming; Morgan Kauffman, San Francisco, CA.

    Book  Google Scholar 

  • Bhattacharya M, Abraham A and Nath B 2001 A linear genetic programming approach for modelling electricity demand prediction in Victoria; In: Proceedings of the hybrid information systems, first international workshop on hybrid intelligent systems, Adelaide, Australia, 379–393.

  • Brameier M and Banzhaf W 2001 A comparison of linear genetic programming and neural networks in medical data mining; IEEE Transactions on Evolutionary Computation 5 17–26.

    Article  Google Scholar 

  • Brameier M 2004 On linear genetic programming; Ph.D. thesis. University of Dortmund.

  • Baiamonte G and Ferro V 2007 Simple flume for flow measurement in sloping channel; J. Irrig. Drain. Eng. 133(1) 71–78.

    Article  Google Scholar 

  • Bordignon S and Lisi F 2000 Nonlinear analysis and prediction of river flow time series; Environmetrics 11 463–477.

    Article  Google Scholar 

  • Charhate S B, Deo M C and Sanil Kumar V 2007 Soft and hard computing approaches for real time prediction of currents in a tide dominated area; J. Engineering for the Maritime Environment 221 147–163.

    Google Scholar 

  • Charhate S B, Deo M C and Londhe S N 2008 Inverse modelling to derive wind parameters from wave measurements; Applied Ocean Research 30(2) 120–129.

    Article  Google Scholar 

  • Cigizoglu H K and Kisi O 2000 Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data; Nordic Hydrology 36(1) 1–16.

    Google Scholar 

  • Cigizoglu H K and Alp M 2006 Generalized regression neural network in modelling river sediment yield; Advanced Engineering Software 37 63–68.

    Article  Google Scholar 

  • Deo O, Jothiprakash V and Deo M C 2008 Genetic Programming to predict spillway scour; Int. J. Tomography and Statistics 8(8) 32–45.

    Google Scholar 

  • Drecourt J P 1999 Application of Neural Networks and Genetic Programming to Rainfall Runoff Modeling; Danish Hydraulic Institute (Hydro-Informatics Techonologies HIT), D2K-0699-1.

  • Firat A 2008 Comparison of artificial intelligence techniques for river flow forecasting; Hydrol. Earth Syst. Sci. 12 123–139.

    Google Scholar 

  • Foster J A 2001 Discipulus: A commercial genetic programming system; Genetic Programming and Evolvable Machines 2 201–203.

    Article  Google Scholar 

  • Gaur S and Deo M C 2008 Real time wave forecasting using genetic programming; Ocean Engineering 35 1166–1172.

    Article  Google Scholar 

  • Guven A, Gunal M and Cevik A 2006 Prediction of pressure fluctuations on stilling basins; Can. J. Civ. Eng. 33(11) 1379–1388.

    Article  Google Scholar 

  • Guven A and Gunal M 2008 A genetic programming approach for prediction of local scour downstream hydraulic structures; J. Irrig. Drain. Eng. 132(4) 241–249.

    Article  Google Scholar 

  • Guven A, Aytek A, Yuce M İ and Aksoy H 2008 Genetic programming-based empirical model for daily reference evapotranspiration estimation; CLEAN-Soil, Air, Water 36(10–11) 905–912.

    Article  Google Scholar 

  • Habib E H and Meselhe E A 2006 Stage-discharge relations for low-gradient tidal streams using data driven models; J. Hydraul. Eng. 132(5) 482–492.

    Article  Google Scholar 

  • Jain K S 2008 Development of integrated discharge and sediment rating relation using a compound neural network; J. Hydrol. Eng. 13(3) 124–131.

    Article  Google Scholar 

  • Jain P and Deo M C 2008 Artificial intelligence tools to forecast ocean waves in real time; The Open Ocean Engineering Journal 1 13–21.

    Article  Google Scholar 

  • Kalra R and Deo M C 2007 Genetic programming to retrieve missing information in wave records along the west coast of India; Applied Ocean Research 29(3) 99–111.

    Article  Google Scholar 

  • Kalra R, Deo M C, Kumar R and Agarwal V K 2008 Genetic programming to estimate coastal waves from deep water measurements; Int. J. Ecology and Development 10(8) 67–76.

    Google Scholar 

  • Kisi O 2004 River flow modelling using artificial neural networks; J. Hydrol. Eng. 9(1) 60–63.

    Article  Google Scholar 

  • Kisi O and Cigizoglu H K 2007 Comparison of different ANN techniques in river flow prediction; Civil Engineering and Environmental Systems 24(3) 211–231.

    Article  Google Scholar 

  • Lopesi H S and Weinert W R 2004 A gene-expression programming system for time series modelling; In: Proc. XXV Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE), Recife, Brazil, CD-ROM Version.

  • Maier H R and Dandy G C 2000 Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications; Environ. Modelling Software 15 101–123.

    Article  Google Scholar 

  • Muttil N and Liong S Y 2004 A superior exploration-exploitation balance in shuffled complex evolution; J. Hydraul. Eng. 130(2) 211–220.

    Article  Google Scholar 

  • Oltean M and Groşan C 2003 A comparison of several linear genetic programming techniques; Complex Systems 14(1) 1–29.

    Google Scholar 

  • Preis A and Otsfeld A 2008 A coupled model tree-genetic algorithm scheme of flow and water quality predictions in watersheds; J. Hydrol. 349 364–375.

    Article  Google Scholar 

  • Rumelhart D E, Hinton G E and Williams R J 1986 Learning internal representations by error propagation; In: Paralled Distributed Processing. Explorations in the Microstructure of Cognition (eds) Rumelhart D E, McClelland J L, and the PDP Research Group, Volume 1: Foundations (Cambridge: MIT Press), pp 318–362.

    Google Scholar 

  • Singh A K, Deo M C and Sanil Kumar V 2007 Neural network — genetic programming for sediment transport; ICE J. Maritime Engineering 160(MA3) 113–119.

    Article  Google Scholar 

  • Specht D F 1991 A general regression neural network; IEEE Trans. Neural Netw. 2(6) 568–576.

    Article  Google Scholar 

  • Tayfur G 2002 Artificial neural networks for sheet sediment transport; Hydrol. Sci. 47(6) 879–892.

    Article  Google Scholar 

  • Tayfur G and Singh V P 2006 ANN and fuzzly logic models for simulating event-based rainfall-ruoff; J. Hydraul. Eng. 132(12) 1321–1330.

    Article  Google Scholar 

  • Tayfur G and Guldal V 2006 Artificial neural networks for estimating daily total suspended sediment in natural streams; Nordic Hydrology 37 69–79.

    Google Scholar 

  • Tayfur G, Moramarco T and Singh V P 2007 Predicting and forecasting flow discharge at sites receiving significant lateral inflow; Hydrological Processes 21 1848–1859.

    Article  Google Scholar 

  • Tayfur G and Moramarco T 2008 Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms; J. Hydrol. 352 77–93.

    Article  Google Scholar 

  • Takens F 1981 Detecting strange attractors in turbulence in turbulence; In: Dynamical systems and turbulence (eds) Hand D and Young L S, Springer-Verlag, Berlin, pp 366.

    Chapter  Google Scholar 

  • United States Geographical Survey (USGS) web server: http://www.usgs.gov

  • Ustoorikar K and Deo M C 2008 Filling up gaps in wave data with genetic programming; Marine Structures 21 177–195.

    Article  Google Scholar 

  • Wang W, Van Gelder P H A J M and Vrijling J K 2004 Periodic autoregressive models applied to daily streamflow; Proceedings of the 6th International Conference on Hydroinformatics, World Scientific, Singapore, 1334–1341.

    Google Scholar 

  • Wang W, Van Gelder P H A J M, Vrijling J K and Ma J 2006 Forecasting daily streamflow using hybrid ANN models; J. Hydrol. 324 383–399.

    Article  Google Scholar 

  • Whigham P A and Crapper P F 2001 Modeling rainfall-runoff using Genetic Programming; Mathematical and Computer Modelling 33 707–721.

    Article  Google Scholar 

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

    Aytac Guven

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  1. Aytac Guven
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Correspondence to Aytac Guven.

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Guven, A. Linear genetic programming for time-series modelling of daily flow rate. J Earth Syst Sci 118, 137–146 (2009). https://doi.org/10.1007/s12040-009-0022-9

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  • DOI: https://doi.org/10.1007/s12040-009-0022-9

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