Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique

[1]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[2]  L. S. Kuchment,et al.  Rainfall−runoff modelling of the Ouse basin, North Yorkshire: an application of a physically based distributed model , 1996 .

[3]  S. Mallat.  A wavelet tour of signal processing , 1998 .

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

[5]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

[6]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[7]  Chul-Hwan Kim,et al.  Wavelet transforms in power systems. I. General introduction to the wavelet transforms , 2000 .

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

[9]  Eduardo A. Varas,et al.  Artificial neural networks for streamflow prediction , 2002 .

[10]  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..

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

[12]  F. Anctil,et al.  An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition , 2004 .

[13]  David Labat,et al.  Recent advances in wavelet analyses: Part 1. A review of concepts , 2005 .

[14]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[15]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

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

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

[18]  W. Banzhaf,et al.  1 Linear Genetic Programming , 2007 .

[19]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

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

[21]  Ozgur Kisi,et al.  Stream flow forecasting using neuro‐wavelet technique , 2008 .

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

[23]  Mohammad H. Aminfar,et al.  A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation , 2009, Eng. Appl. Artif. Intell..

[24]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence , 2009 .

[25]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[26]  Donna M. Rizzo,et al.  Advances in ungauged streamflow prediction using artificial neural networks , 2010 .

[27]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[28]  Amir Hossein Alavi,et al.  Comment on ‘Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623–628’ , 2010 .

[29]  Shreenivas Londhe,et al.  Comparison of data-driven modelling techniques for river flow forecasting , 2010 .

[30]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[31]  Mehmet Özger,et al.  Significant wave height forecasting using wavelet fuzzy logic approach , 2010 .

[32]  Vahid Nourani,et al.  Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model , 2010 .

[33]  Bithin Datta,et al.  Coupled simulation‐optimization model for coastal aquifer management using genetic programming‐based ensemble surrogate models and multiple‐realization optimization , 2011 .

[34]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2011, Neural Computing and Applications.

[35]  Ozgur Kisi,et al.  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process , 2011 .

[36]  R. Khosa,et al.  Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach , 2012 .

[37]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[38]  A. Tahershamsi,et al.  An evaluation model of artificial neural network to predict stable width in gravel bed rivers , 2012, International Journal of Environmental Science and Technology.

[39]  Ercan Kahya,et al.  Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey , 2012 .

[40]  Vahid Nourani,et al.  Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process , 2012 .

[41]  Milan Onderka,et al.  Seepage velocities derived from thermal records using wavelet analysis , 2013 .

[42]  Vahid Nourani,et al.  Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling , 2013 .