A machine code-based genetic programming for suspended sediment concentration estimation

Correct estimation of suspended sediment concentration carried by a river is very important for many water resources projects. The application of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, for suspended sediment concentration estimation is proposed in this paper. The LGP is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, operated by the US Geological Survey (USGS) are used as case studies. The root mean square errors (RMSE) and determination coefficient (R^2) statistics are used for evaluating the accuracy of the models. Comparison of the results indicated that the LGP performs better than the neuro-fuzzy, neural networks and rating curve models. For the Rio Valenciano and Quebrada Blanca Stations, it is found that the LGP models with RMSE=44.4mg/l, R^2=0.910 and RMSE=13.9mg/l, R^2=0.952 in test period is superior in estimating daily suspended sediment concentrations than the best accurate neuro-fuzzy model with RMSE=52.0mg/l, R^2=0.876 and RMSE=17.9mg/l, R^2=0.929, respectively.

[1]  O. Kisi,et al.  A genetic programming approach to suspended sediment modelling , 2008 .

[2]  Deg-Hyo Bae,et al.  Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique , 2007 .

[3]  Yaqub Rafiq,et al.  An Evolutionary Computation Approach to Sediment Transport Modelling , 2006 .

[4]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

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

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

[7]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[8]  Peter Nordin,et al.  A compiling genetic programming system that directly manipulates the machine-code , 1994 .

[9]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[10]  Aytac Guven,et al.  Linear genetic programming for time-series modelling of daily flow rate , 2009 .

[11]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[12]  Daniel Rivero,et al.  Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp , 2003, Appl. Artif. Intell..

[13]  N. K. Goel,et al.  Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship , 2006 .

[14]  K. P. Sudheer,et al.  Fuzzy model identification based on cluster estimation for reservoir inflow forecasting , 2008 .

[15]  Ozgur Kisi,et al.  Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data , 2009 .

[16]  Li-Chiu Chang,et al.  Real‐time recurrent learning neural network for stream‐flow forecasting , 2002 .

[17]  Zhiye Zhao,et al.  An optimal neural network and concrete strength modeling , 2002 .

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

[19]  Ali Aytek,et al.  New Approach for Stage–Discharge Relationship: Gene-Expression Programming , 2009 .

[20]  K. P. Sudheer,et al.  Identification of physical processes inherent in artificial neural network rainfall runoff models , 2004 .

[21]  Vladan Babovic,et al.  Declarative and Preferential Bias in GP-based Scientific Discovery , 2002, Genetic Programming and Evolvable Machines.

[22]  O. Giustolisi.  Using genetic programming to determine Chèzy resistance coefficient in corrugated channels , 2004 .

[23]  C. Sivapragasam,et al.  Genetic programming approach for flood routing in natural channels , 2008 .

[24]  Daniel Rivero,et al.  Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks , 2007 .

[25]  Peter Nordin,et al.  Evolutionary program induction of binary machine code and its applications , 1997 .

[26]  O. Kisi.  River flow forecasting and estimation using different artificial neural network techniques , 2008 .

[27]  Vladan Babovic,et al.  Data Mining and Knowledge Discovery in Sediment Transport , 2000 .

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

[29]  Mustafa Gunal,et al.  Prediction of Scour Downstream of Grade-Control Structures Using Neural Networks , 2008 .

[30]  Ozgur Kisi,et al.  Modeling River Stage‐Discharge Relationships Using Different Neural Network Computing Techniques , 2009 .

[31]  Frank D. Francone,et al.  Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming , 2004, Inf. Sci..

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

[33]  John R. Koza,et al.  Genetic Programming II , 1992 .

[34]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

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

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

[37]  F. Chang,et al.  Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks , 2009 .

[38]  H. Md. Azamathulla,et al.  Genetic Programming Approach to Predict Sediment Concentration for Malaysian Rivers , 2010 .

[39]  H. Md. Azamathulla,et al.  Linear genetic programming for prediction of circular pile scour , 2009 .

[40]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

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

[42]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

[43]  Peter Nordin,et al.  Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code , 1995, ICGA.

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