Suspended sediment modeling using genetic programming and soft computing techniques

[1]  Robert B. Thomas.  Estimating Total Suspended Sediment Yield With Probability Sampling , 1985 .

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Edward A. McBean,et al.  Uncertainty in Suspended Sediment Transport Curves , 1988 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  John F. Kennedy,et al.  Menu of Coupled Velocity and Sediment‐Discharge Relations for Rivers , 1990 .

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

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

[8]  Peter F. Ffolliott,et al.  SEDIMENT RATING CURVES FOR A CLEARCUT PONDEROSA PINE WATERSHED IN NORTHERN ARIZONA1 , 1993 .

[9]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

[10]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[11]  J. Bathurst,et al.  SHESED: a physically based, distributed erosion and sediment yield component for the SHE hydrological modelling system , 1996 .

[12]  Ranvir Singh,et al.  Estimation of temporal variation of sediment yield from small catchments through the kinematic method , 1997 .

[13]  J. Refsgaard.  Parameterisation, calibration and validation of distributed hydrological models , 1997 .

[14]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[17]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[18]  Dragan Savic,et al.  A Genetic Programming Approach to Rainfall-Runoff Modelling , 1999 .

[19]  N. Asselman.  Fitting and interpretation of sediment rating curves , 2000 .

[20]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[21]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[22]  Sharad K. Jain,et al.  Development of Integrated Sediment Rating Curves Using ANNs , 2001 .

[23]  Vladan Babovic,et al.  Neural networks as routine for error updating of numerical models , 2001 .

[24]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[25]  Cécile Picouet,et al.  Empirical and conceptual modelling of the suspended sediment dynamics in a large tropical African river: the Upper Niger river basin , 2001 .

[26]  Cândida Ferreira.  Gene Expression Programming in Problem Solving , 2002 .

[27]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[28]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation and Forecasting using Artificial Neural Networks , 2002 .

[29]  Vladan Babovic,et al.  Velocity predictions in compound channels with vegetated floodplains using genetic programming , 2003 .

[30]  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 .

[31]  Özlem Terzi,et al.  Fuzzy Logic Model Approaches to Daily Pan Evaporation Estimation in Western Turkey , 2004 .

[32]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[33]  A. Petersen-Øverleir.  Accounting for heteroscedasticity in rating curve estimates , 2004 .

[34]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[35]  M. Keskin,et al.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue , 2004 .

[36]  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 .

[37]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[38]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[39]  Vladan Babovic,et al.  Rainfall‐Runoff Modeling Based on Genetic Programming , 2006 .

[40]  Bellie Sivakumar,et al.  Suspended sediment load estimation and the problem of inadequate data sampling: a fractal view , 2006 .

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

[42]  M. Demissie,et al.  The accuracy of sediment loads when log-transformation produces nonlinear sediment load–discharge relationships , 2007 .

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

[44]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

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

[46]  Riccardo Poli,et al.  CES-480 Covariant Parsimony Pressure for Genetic Programming , 2008 .

[47]  Ozgur Kisi,et al.  Evolutionary fuzzy models for river suspended sediment concentration estimation. , 2009 .

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

[49]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[50]  Mohammad Ali Ghorbani,et al.  Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) , 2011 .

[51]  H Md Azamathulla,et al.  ANFIS-based approach for the estimation of transverse mixing coefficient. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.

[52]  Özgür Kisi,et al.  Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations , 2011, Comput. Geosci..

[53]  Ozgur Kisi,et al.  Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models , 2011 .

[54]  Ozgur Kisi,et al.  Prediction of Short-Term Operational Water Levels Using an Adaptive Neuro-Fuzzy Inference System , 2011 .

[55]  H. Md. Azamathulla,et al.  Support vector machine approach for longitudinal dispersion coefficients in natural streams , 2011, Appl. Soft Comput..

[56]  Hazi Mohammad Azamathulla,et al.  ANFIS-Based Approach for Predicting the Scour Depth at Culvert Outlets , 2011 .

[57]  O. Kisi,et al.  Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain) , 2012 .

[58]  H. Md. Azamathulla,et al.  ANFIS-based approach for predicting sediment transport in clean sewer , 2012, Appl. Soft Comput..

[59]  H. Md. Azamathulla,et al.  Gene-expression programming for transverse mixing coefficient , 2012 .