Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 3
Abstract
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 cumbersome 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 considering 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.
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References
Ajmera, T. K., and Goyal, M. K. (2012). “Development of stage discharge rating curve using model tree and neural networks: An application to Peachtree Creek in Atlanta.” Expert Syst. Appl., 39(5), 5702–5710.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology-I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology-II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
Azamathulla, Hazi. Md., Ghani, A. Ab., Chang, C. K., Hasan, Z. A., Zakaria, N. A. (2010). “Machine learning approach to predict sediment load—A case study.” Clean—Soil, Air, Water, 38(10), 969–976.
Chen, S., Cowan, C. F. N., and Grant, P. M. (1991). “Orthogonal least squares learning algorithm for radial basis function networks.” IEEE Trans. Neural Network, 2(2), 302–309.
Chiu, S. (1994). “Fuzzy model identification based on cluster estimation.” J. Intell. Fuzzy Sys., 2(3), 267–278.
Cigizoglu, H. K. (2002). “Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves.” Turk., J. Eng. Environ. Sci., 26(1), 27–36.
Cigizoglu, H. K., and Kisi, O. (2006). “Methods to improve the neural network performance in suspended sediment estimation.” J. Hydrol. (Amsterdam), 317(3-4), 221–228.
Cobaner, M., Unal, B., and Kisi, O. (2009). “Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data.” J. Hydrol. (Amsterdam), 367(1-2), 52–61.
Daud, M. N. R., and Corne, D. W. (2007). “Human readable rule induction in medical data mining: A survey of existing algorithms.” Proc., WSEAS European Computing Conf., Springer LNEE, Vol. 1, 787–798.
Fernando, D. A. K., and Shamseldin, A. Y. (2009). “Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models.” J. Hydrol. Eng., 14(3), 286–292.
Gao, P., and Pasternack, G. (2007). “Dynamics of suspended sediment transport at field-scale drain channels of irrigation-dominated watersheds in the Sonoran Desert, southeastern California.” Hydrol. Process., 21(16), 2081–2092.
Govindaraju, R. S., and Rao, A. R. (2000). Artificial neural networks in hydrology, Kluwer Academic, Dordrecht, the Netherlands.
Goyal, M. K., and Ojha, C. S. P. (2010) “Analysis of mean monthly rainfall runoff data of Indian catchments using dimensionless variables by neural network.” J. Environ. Protect., 1(2), 155–171.
Goyal, M. K., and Ojha, C. S. P. (2012a). “Downscaling of surface temperature for lake catchment in arid region in India using linear multiple regression and neural networks.” Int. J. Climatol., in press.
Goyal, M. K., Ojha, C. S. P., and Burn Donald, H. (2010). “An evaluation of decision tree algorithm as a downscaling tool: Application on a Lake Basin for an arid region in India.” 10th Conf. Canadian Geophysical Union, University of Guelph, ON, Canada.
Goyal, M. K., and Ojha, C. S. P. (2012b). “Downscaling of precipitation on a Lake Basin: Evaluation of rule and decision tree induction algorithms.” Hydrol. Res., 43, in press.
Goyal, M. K., and Ojha, C. S. P. (2011). “Estimation of scour downstream of a ski-jump bucket using support vector and m5 model tree.” Water Resour. Manage., 25(9), 2177–2195.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feed forward networks with the Marquardt algorithm.” IEEE Trans. Neural Networks, 5(6), 989–993.
Haghizadeh, A., Shui, L. T., and Goudarzi, E. (2010). “Estimation of yield sediment using artificial neural network at basin scale.” Australian J. Basic Appl. Sci., 4(7), 1668–1675.
Hsu, K-L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Jain, A., and Srinivasulu, S. (2004). “Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques.” Water Resour. Res., 40(4), W04302, 1–12.
Jain, S. K. (2001). “Development of integrated discharge and sediment rating relation using a Compound Neural Network.” J. Hydrol. Eng., 127(1), 30–37.
Jain, S. K. (2008). “Development of integrated sediment rating curves using ANNs.” J. Hydrol. Eng., 13(3), 124–131.
Jain, S. K., Singh, V. P., and Genuchten, M. T. V. (2004). “Analysis of soil water retention data using artificial neural networks.” J. Hydrol. Eng., 9(5), 415–420.
Kisi, O. (2004). “Multi-Layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation.” Hydrol. Sci. J., 49(6), 1025–1040.
Kisi, O. (2005). “Suspended sediment estimation using neuro-fuzzy and neural network approaches.” Hydrol. Sci. J., 50(4), 683–696.
Kisi, O. (2010). “River suspended sediment concentration modelling using a neural differential evolution approach.” J. Hydrol. (Amsterdam), 389(1–2), 227–235.
Kisi, O., Karahan, M. E., and Sen, Z. (2006). “River suspended sediment modelling using a fuzzy logic approach.” Hydrol. Process., 20(20), 4351–4362.
Klir, J., and Foger, T. A. (1988). Fuzzy sets, uncertainty, and information, Prentice-Hall, Englewood Cliffs, New Jersey.
Kothyari, U. C., Jain, M. K., and Ranga Raju, K. G. (2002). “Estimation of temporal variation of soil erosion and sediment yield using GIS.” Hydrol. Sci. J., IAHS, 47(5), 693–706.
Lohani, A. K., Goel, N. K., and Bhatia, K. K. S. (2007). “Deriving stage-discharge-sediment concentration relationships using Fuzzy logic.” Hydrol. Sci. J., 52(4), 793–807.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.” Environ. Model. Software, 15(1), 101–124.
Metternicht, G. (2001). “Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS Foundations of an expert system.” Ecol. Model., 144(2-3), 163–179.
Mianaei, S. J., and Keshavarzi, A. R. (2009). “Prediction of riverine suspended sediment discharge using fuzzy logic algorithms, and some implications for estuarine setting.” Geo.-Mar. Lett., 30(1), 35–45.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall runoff models.” Hydrol. Sci. J., 41(3), 399–418.
Nagy, H. M., Watanabe, K., and Hirano, M. (2002). “Prediction of sediment load concentration in rivers using artificial neural networks.” J. Hydrol. Eng., 128(6), 588–595.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models: 1. A discussion of principles.” J. Hydrol. (Amsterdam), 10(3), 282–290.
Ojha, C. S. P., Goyal, M. K., and Kumar, S. (2007). “Applying Fuzzy logic and the point count system to select landfill sites.” Environ. Monit. Assess., 135(1–3), 99–106.
Parajuli, P. B., Nelson, N. O., Frees, L. D., and Mankin, K. R. (2009). “Comparison of AnnAGNPS and SWAT model simulation results in USDA-CEAP agricultural watersheds in south-central Kansas.” Hydrol. Process., 23(5), 748–763.
Quinlan, J. R. (1992). “Learning with continuous classes.” Proc. 5th Australian Joint Conf. on Artificial Intelligence, World Scientific, Singapore, 343–348.
Raghuwanshi, N. S., Singh, R., and Reddy, L. S. (2006). “Runoff and sediment yield modelling using artificial neural networks: Upper Siwane River, India.” J. Hydrol. Eng., 11(1), 71–79.
Rumelhart, D. E., Hinton, E., and Williams, J. (1986). Learning internal representation by error propagation, parallel distributed processing, Vol. 1, MIT, Cambridge, MA, 318–362.
Salas, J. D., Delleur, J. W., Yevjevich, V., and Lane, W. L. (1980). Applied modelling of hydrologic time series, Water Resources Publications, P. O. Box 2841, Littleton, CO 80161.
Senthil Kumar, A. R., Sudheer, K. P., Jain, S. K., and Agarwal, P. K. (2005). “Rainfall-runoff modelling using artificial neural networks: Comparison of network types.” Hydrol. Process., 19(6), 1277–1291.
Shahin, M. M. A. (1993). “An overview of reservoir sedimentation in some African river basins. Sediment problems: Strategies for monitoring, prediction and control.” Proc., Yokohama Symp., No. 217, IAHS, Institute of Hydrology, Wallingford, Oxfordshire, UK, 93–100.
Singh, K. K., Pal, M., Ojha, C. S. P., and Singh, V. P. (2008). “Estimation of removal efficiency for settling basins using neural networks and support vector machines.” J. Hydrol. Eng., 13(3), 146–155.
Smith, H. G. (2008). “Estimation of suspended sediment loads and delivery in an incised upland headwater catchment, south-eastern Australia.” Hydrol. Process., 22(16), 3135–3148.
Srinivasulu, S., and Jain, A. (2009). “River flow prediction using an integrated approach.” J. Hydrol. Eng., 14(1), 75–83.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2002). “A data-driven algorithm for constructing artificial neural network rainfall-runoff models.” Hydrol. Process., 16(6), 1325–1330.
Tayfur, G. (2001). “Modelling two-dimensional erosion process over infiltrating surfaces.” J. Hydrol. Eng., 6(3), 259–262.
Tayfur, G. (2002). “Applicability of sediment transport capacity models for nonsteady state erosion from steep slopes.” J. Hydrol. Eng., 7(3), 252–259.
Tayfur, G., Ozdemir, S., and Singh, V. P. (2003). “Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces.” Adv. Water Resour., 26(12), 1249–1256.
Tejwani, K. G. (1984). “Reservoir sedimentation in india-Its causes, control, and future course of action.” Water Int., 9(4), 150–154.
The MathWorks (2001). ANN Toolbox User’s Guide, 3 Apple Hill Drive, Natick, MA 01760-2098.
Thirumalaiah, K., and Deo, M. C. (1998). “River stage forecasting using artificial neural networks.” J. Hydrol. Eng., 3(1), 26–32.
Vos, N. J., and Rientjes, T. H. M. (2008). “Multiobjective training of artificial neural networks for rainfall-runoff modelling.” Water Resour. Res., 44, W08434, 1–15.
Witten, I. H., and Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd Ed., Morgan Kaufmann, San Francisco.
Zadeh, L. A. (1965). “Fuzzy sets.” Inform. Control, 8(3), 338–353.
Zadeh, L. A. (1983). “The role of fuzzy logic in the management of uncertainty in expert systems.” Fuzzy Sets Syst., 11(1–3), 197–198.
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Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 3 • March 2012
Pages: 394 - 404
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jul 15, 2009
Accepted: May 25, 2011
Published online: Jul 1, 2011
Published in print: Mar 1, 2012
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