Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach

This chapter proposes fuzzy genetic approach so as to predict suspended sediment concentration (SSC) carried in natural rivers for a given stream cross section. Fuzzy genetic models are improved by combining two methods, fuzzy logic and genetic algorithms. The accuracy of fuzzy genetic models was compared with those of the adaptive network-based fuzzy inference system, multilayer perceptrons, and sediment rating curve models. The daily streamflow and suspended sediment data belonging to two stations, Muddy Creek near Vaughn (Station No: 06088300) and Muddy Creek at Vaughn (Station No: 06088500), operated by the US Geological Survey were used as case studies. The root mean square errors and determination coefficient statistics were used for evaluating the accuracy of the models. The comparison results revealed that the fuzzy genetic approach performed better than the other models in the estimation of the SSC.

[1]  Ozgur Kisi,et al.  Fuzzy Genetic Approach for Modeling Reference Evapotranspiration , 2010 .

[2]  Galip Seckin,et al.  Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels , 2010, Adv. Eng. Softw..

[3]  A. K. Lohani,et al.  Deriving stage–discharge–sediment concentration relationships using fuzzy logic , 2007 .

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

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

[6]  V. Singh,et al.  Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces , 2003 .

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

[8]  Özgür Kişi,et al.  Daily suspended sediment modelling using a fuzzy differential evolution approach / Modélisation journalière des matières en suspension par une approche d’évolution différentielle floue , 2004 .

[9]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[10]  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 .

[11]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

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

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  Özgür Kisi,et al.  Adaptive neuro-fuzzy computing technique for suspended sediment estimation , 2009, Adv. Eng. Softw..

[15]  J. B. Kiszka,et al.  The influence of some fuzzy implication operators on the accuracy of a fuzzy model-part II , 1985 .

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

[17]  Arup Kumar Sarma,et al.  Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir , 2005 .

[18]  Robert Sandy,et al.  Statistics for Business and Economics , 1989 .

[19]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[20]  Zekai Şen,et al.  Fuzzy algorithm for estimation of solar irradiation from sunshine duration , 1998 .

[21]  Vahid Nourani,et al.  Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. , 2009, The Science of the total environment.

[22]  Jeanne S. Yulianti,et al.  Waste-Load Allocation Using Genetic Algorithms , 2001 .

[23]  Abdüsselam Altunkaynak,et al.  Sediment load prediction by genetic algorithms , 2009, Adv. Eng. Softw..

[24]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

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

[26]  Özgür Kisi,et al.  Constructing neural network sediment estimation models using a data-driven algorithm , 2008, Math. Comput. Simul..

[27]  Mehmet Özger,et al.  Comparison of fuzzy inference systems for streamflow prediction , 2009 .

[28]  Ozgur Kisi,et al.  Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l'aide de plusieurs techniques empiriques , 2008 .

[29]  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 .

[30]  Veysel Güldal,et al.  2D Unit Sediment Graph Theory , 2001 .

[31]  Ozgur Kisi,et al.  River suspended sediment modelling using a fuzzy logic approach , 2006 .

[32]  O. Kisi,et al.  Predicting the compressive strength of steel fiber added lightweight concrete using neural network , 2008 .

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

[34]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .

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

[36]  Mahmud Güngör,et al.  Monthly total sediment forecasting using adaptive neuro fuzzy inference system , 2010 .

[37]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .