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

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.

[1]  M. Deo,et al.  Genetic programming for retrieving missing information in wave records along the west coast of India , 2007 .

[2]  Pieter van Gelder,et al.  PERIODIC AUTOREGRESSIVE MODEL APPLIED TO DAILY STREAMFLOW , 2004 .

[3]  H. Akaike.  A new look at the statistical model identification , 1974 .

[4]  Ali Aytek,et al.  An application of artificial intelligence for rainfall-runoff modeling , 2008 .

[5]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[6]  Nitin Muttil,et al.  Superior exploration-exploitation balance in shuffled complex evolution , 2004 .

[7]  Mustafa Gunal,et al.  Prediction of pressure fluctuations on sloping stilling basins , 2006 .

[8]  Mustafa Gunal,et al.  Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures , 2008 .

[9]  M. Firat,et al.  Comparison of Artificial Intelligence Techniques for river flow forecasting , 2008 .

[10]  James A. Foster,et al.  Review: Discipulus: A Commercial Genetic Programming System , 2001, Genetic Programming and Evolvable Machines.

[11]  Vito Ferro,et al.  Simple Flume for Flow Measurement in Sloping Open Channel , 2007 .

[12]  Raouf N. Gorgui-Naguib,et al.  A general regression neural network analysis of prognostic markers in prostate cancer , 1998, Neurocomputing.

[13]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[14]  Francesco Lisi,et al.  Nonlinear analysis and prediction of river flow time series , 2000 .

[15]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[16]  H. S. Lopes,et al.  A GENE EXPRESSION PROGRAMMING SYSTEM FOR TIME SERIES MODELING , 2004 .

[17]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[18]  Vijay P. Singh,et al.  Predicting and forecasting flow discharge at sites receiving significant lateral inflow , 2007 .

[19]  Emad Habib,et al.  Stage¿Discharge Relations for Low-Gradient Tidal Streams Using Data-Driven Models , 2006 .

[20]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[21]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[22]  Makarand Deo,et al.  Neural network–genetic programming for sediment transport , 2007 .

[23]  M. C. Deo,et al.  Filling up gaps in wave data with genetic programming , 2008 .

[24]  Hafzullah Aksoy,et al.  Genetic Programming‐Based Empirical Model for Daily Reference Evapotranspiration Estimation , 2008 .

[25]  Tommaso Moramarco,et al.  Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms , 2008 .

[26]  A. N. Sharkovskiĭ.  Dynamic systems and turbulence , 1989 .

[27]  Peter A. Whigham,et al.  Modelling rainfall-runoff using genetic programming , 2001 .

[28]  Sharad K. Jain,et al.  Development of Integrated Discharge and Sediment Rating Relation Using a Compound Neural Network , 2008 .

[29]  Mihai Oltean,et al.  A Comparison of Several Linear Genetic Programming Techniques , 2003, Complex Syst..

[30]  M. C. Deo,et al.  Real-time wave forecasting using genetic programming , 2008 .

[31]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[32]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[33]  Hafzullah Aksoy,et al.  An explicit neural network formulation for evapotranspiration , 2008 .

[34]  Gokmen Tayfur,et al.  Artificial neural networks for estimating daily total suspended sediment in natural streams , 2006 .

[35]  Avi Ostfeld,et al.  A coupled model tree-genetic algorithm scheme for flow and water quality predictions in watersheds , 2008 .

[36]  Vijay P. Singh,et al.  ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff , 2006 .

[37]  V. Jothiprakash,et al.  Genetic Programming to Predict Spillway Scour , 2008 .

[38]  Vijay K. Agarwal,et al.  Genetic Programming to Estimate Coastal Waves from Deep Water Measurements , 2008 .

[39]  Ajith Abraham,et al.  A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria , 2001, HIS.

[40]  Makarand Deo,et al.  Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area , 2007 .

[41]  Makarand Deo,et al.  Artificial Intelligence Tools to Forecast Ocean Waves in Real Time , 2008 .

[42]  Markus Brameier,et al.  On linear genetic programming , 2005 .

[43]  Ozgur Kisi,et al.  Comparison of different ANN techniques in river flow prediction , 2007 .

[44]  G. Tayfur.  Artificial neural networks for sheet sediment transport , 2002 .

[45]  Makarand Deo,et al.  Inverse modeling to derive wind parameters from wave measurements , 2008 .

[46]  Wolfgang Banzhaf,et al.  A comparison of linear genetic programming and neural networks in medical data mining , 2001, IEEE Trans. Evol. Comput..