Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms

The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cum- bersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived con- sidering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers. DOI: 10.1061/(ASCE)HE.1943-5584.0000445. © 2012 American Society of Civil Engineers. CE Database subject headings: Suspended sediment; Neural networks; Fuzzy sets; Reservoirs. Author keywords: Suspended sediment concentration; Neural networks; Fuzzy Logic; M5; REPTree; Bhakra reservoir.

[1]  G. Tayfur.  Applicability of Sediment Transport Capacity Models for Nonsteady State Erosion from Steep Slopes , 2002 .

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

[3]  Vijay P. Singh,et al.  Estimation of Removal Efficiency for Settling Basins Using Neural Networks and Support Vector Machines , 2008 .

[4]  G. Pasternack,et al.  Dynamics of suspended sediment transport at field‐scale drain channels of irrigation‐dominated watersheds in the Sonoran Desert, southeastern California , 2007 .

[5]  Manish Kumar Goyal,et al.  Development of stage-discharge rating curve using model tree and neural networks: An application to Peachtree Creek in Atlanta , 2012, Expert Syst. Appl..

[6]  David Corne,et al.  Human Readable Rule Induction in Medical Data Mining , 2009 .

[7]  Daud Mnr.  Human Readable Rule Induction in Medical Data Mining: A Survey of Existing Algorithms , 2007 .

[8]  Graciela Metternicht,et al.  Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system , 2001 .

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

[11]  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 .

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

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

[14]  Sunil Kumar,et al.  Applying Fuzzy logic and the point count system to select landfill sites , 2007, Environmental monitoring and assessment.

[15]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[16]  Manish Kumar Goyal,et al.  Analysis of Mean Monthly Rainfall Runoff Data of Indian Catchments Using Dimensionless Variables by Neural Network , 2010 .

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

[18]  Manish Kumar Goyal,et al.  Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms , 2012 .

[19]  N. J. de Vos,et al.  Multiobjective training of artificial neural networks for rainfall‐runoff modeling , 2008 .

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

[21]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[22]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[23]  A. Keshavarzi,et al.  Prediction of riverine suspended sediment discharge using fuzzy logic algorithms, and some implications for estuarine settings , 2010 .

[24]  Vijay P. Singh,et al.  Analysis of Soil Water Retention Data Using Artificial Neural Networks , 2004 .

[25]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

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

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

[28]  Gokmen Tayfur,et al.  Modeling Two-Dimensional Erosion Process over Infiltrating Surfaces , 2001 .

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

[30]  Ashu Jain,et al.  River Flow Prediction Using an Integrated Approach , 2009 .

[31]  U. C. Kothyari,et al.  Estimation of temporal variation of sediment yield using GIS / Estimation de la variation temporelle de l'exportation sédimentaire grâce à un SIG , 2002 .

[32]  Ozgur Kisi,et al.  River suspended sediment concentration modeling using a neural differential evolution approach , 2010 .

[33]  Narendra Singh Raghuwanshi,et al.  Runoff and Sediment Yield Modeling using Artificial Neural Networks: Upper Siwane River, India , 2006 .

[34]  Asaad Y. Shamseldin,et al.  Investigation of Internal Functioning of the Radial-Basis-Function Neural Network River Flow Forecasting Models , 2009 .

[35]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[36]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[37]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[38]  Manish Kumar Goyal,et al.  Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks , 2012 .

[39]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

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

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

[42]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[43]  K. Tejwani.  Reservoir Sedimentation in India—Its Causes, Control, and Future Course of Action , 1984 .

[44]  Hugh G. Smith,et al.  Estimation of suspended sediment loads and delivery in an incised upland headwater catchment, south‐eastern Australia , 2008 .

[45]  H. Nagy,et al.  Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model , 2002 .

[46]  Chandra Shekhar P. Ojha,et al.  Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree , 2011 .

[47]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .

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

[49]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[50]  Chun Kiat Chang,et al.  Machine Learning Approach to Predict Sediment Load – A Case Study , 2010 .

[51]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[52]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[53]  Kyle R. Mankin,et al.  Comparison of AnnAGNPS and SWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas , 2009 .

[54]  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..

[55]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation for Rivers using Artificial Neural Networks and Sediment Rating Curves , 2002 .

[56]  Ali Haghizadeh,et al.  Estimation of yield sediment using artificial neural network at basin scale. , 2010 .