Ensemble Wavelet-Support Vector Machine Approach for Prediction of Suspended Sediment Load Using Hydrometeorological Data
Publication: Journal of Hydrologic Engineering
Volume 22, Issue 7
Abstract
Explicit prediction of the suspended sediment loads in rivers or streams is very crucial for sustainable water resources and environmental systems. Suspended sediments are a governing factor for the design and operation of hydraulic structures, like canals, diversions and dams. In recent decades, to model hydrological phenomena which are complex in nature the machine learning models are used commonly. In the present study, support vector machine (SVM) with wavelet transform (WASVM) has been employed for prediction of daily suspended sediment load (SL) for two south Indian watersheds (Marol and Muneru) using hydrometeorological data. A 40-year daily observed data (1972–2011) have been used for the analysis, where past SL, streamflow (), and rainfall () data were used as the model inputs, and SL was the model output. Using conventional correlation coefficient analysis between input and output variables, the best input of WASVM model was identified. The reliability of SVM and WASVM models were evaluated on the basis of different performance criteria, i.e., coefficient of determination (), root mean square error (RMSE), normalized mean square error (NMSE), and Nash-Sutcliffe coefficient (NS). Initially, 1-day ahead SL prediction was performed using the best WASVM model. The results showed that, 1-day predictions were very precise, showing a close agreement with the observed SL data (, for the Marol watershed, and , for the Muneru watershed) in the testing period. The same WASVM model was then used for the prediction of SL for the higher lead periods. The NMSE value for the Marol watershed was found as low as 0.06 for 1-day ahead prediction, and increases subsequently as 0.29, 0.46, and 0.70 for 3-, 6-, and 9-day higher leads, respectively. Likewise, for the Muneru watershed, the NMSE value was found as low as 0.21 for 1-day ahead prediction, and increases subsequently as 0.42, 0.53, and 0.68 for 3-, 6-, and 9-day higher leads, respectively. Further, the model was evaluated on the basis of its capability of predicting peak SL and cumulative SL for 1- to 6-day leads. The statistical analysis shows that the developed WASVM model can predict the target value successfully up to a 6-day lead and is not suitable for higher lead specifically in the selected watersheds having similar hydroclimatic conditions like the ones selected in this study. Predictions by the WASVM model were found significantly superior to the ones obtained by the conventional SVM model. The results revealed that the WASVM model provides a very good accuracy in predicting SL and can be used as an effective forecasting tool for hydrological applications.
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References
Adamowski, J., and Chan, H. F. (2011). “A wavelet neural network conjunction model for ground water level forecasting.” J. Hydrol., 407(1), 28–40.
Adamowski, J., and Karapataki, C. (2010). “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms.” J. Hydrol. Eng., 729–743.
Chang, C. C., and Lin, C. J. (2011). “LIBSVM: A library for support vector machines.” ACM Trans. Intell. Syst. Technol., 2(3), 1–27.
Chen, S. T., Yu, P. S., and Tang, Y. H. (2010). “Statistical downscaling of daily precipitation using support vector machines and multivariate analysis.” J. Hydrol., 385(1), 13–22.
Choy, K. Y., and Chan, C. W. (2003). “Modelling of river discharges and rainfall using radial basis function networks based on support vector regression.” Int. J. Syst. Sci., 34(14–15), 763–773.
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multilayer perceptrons.” Adv. Water Resour., 27(2), 185–195.
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., 367(1), 52–61.
Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D., and Summers, R. M. (1992). “The validity of a simple statistical model for estimating fluvial constituent loads: An empirical study involving nutrient loads entering Chesapeake Bay.”Water Resour. Res., 28(9), 2353–2363.
Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, New York.
Feng, Q., Wen, X., and Li, J. (2015). “Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions.” Water Resour. Manage., 29(4), 1049–1065.
Haji, M. S., Mirbagheri, S. A., Javid, A. H., Khezri, M., and Najafpour, G. D. (2013). “A wavelet support vector machine combination model for daily suspended sediment forecasting.” Int. J. Eng.-Trans. C: Aspects, 27(6), 855–864.
Hejazi, M. I., and Cai, X. (2009). “Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm.” Adv. Water Resour., 32(4), 582–593.
Hipni, A., El-shafie, A., Najah, A., Karim, O. A., Hussain, A., and Mukhlisin, M. (2013). “Daily forecasting of dam water levels: Comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS).” Water Resour. Manage., 27(10), 3803–3823.
Huang, G. B., Zhu, Q. Y., and Siew, C. K. (2004). “Extreme learning machine: A new learning scheme of feed-forward neural networks.” Proc., Int. Joint Conf. on Neural Networks (IJCNN2004), Vol. 2, Budapest, Hungary, 985–990.
Huang, G. B., Zhu, Q. Y., and Siew, C. K. (2006). “Extreme learning machine: Theory and applications.” Neurocomputing, 70(1), 489–501.
Jain, S. K. (2001). “Development of integrated sediment rating curves using ANNs.” J. Hydraul. Eng., 30–37.
Jain, S. K. (2012). “Modeling river stage–discharge–sediment rating relation using support vector regression.” Hydrol. Res., 43(6), 851.
Jensen, F. V. (1996). An introduction to Bayesian networks, UCL Press, London.
Kalteh, A. M. (2013). “Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform.” Comput. Geosci., 54, 1–8.
Karamouz, M., Ahmadi, A., and Moridi, A. (2009). “Probabilistic reservoir operation using Bayesian stochastic model and support vector machine.” Adv. Water Resour., 32(11), 1588–1600.
Kecman, V. (2001). Learning and soft computing: Support vector machines, neural networks, and fuzzy logic models, MIT Press, Cambridge, MA.
Khan, M. S., and Coulibaly, P. (2006). “Application of support vector machine in lake water level prediction.” J. Hydrol. Eng., 199–205.
Kisi, O., and Cimen, M. (2011). “A wavelet-support vector machine conjunction model for monthly streamflow forecasting.” J. Hydrol., 399(1), 132–140.
Kisi, O., and Cimen, M. (2012). “Precipitation forecasting by using wavelet-support vector machine conjunction model.” Eng. Appl. Artif. Intell., 25(4), 783–792.
Kişi, Ö. (2009). “Evolutionary fuzzy models for river suspended sediment concentration estimation.” J. Hydrol., 372(1), 68–79.
Kişi, Ö. (2010). “River suspended sediment concentration modeling using a neural differential evolution approach.” J. Hydrol., 389(1), 227–235.
Klir, G., and Yuan, B. (1995). Fuzzy sets and fuzzy logic, Vol. 4, Prentice Hall, Upper Saddle River, NJ.
Koller, D., and Friedman, N. (2009). Probabilistic graphical models: Principles and techniques, MIT Press, Cambridge, MA.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection, Vol. 1, MIT Press, Cambridge, MA.
Kumar, D., Pandey, A., Sharma, N., and Flügel, W. A. (2014). “Modeling suspended sediment using artificial neural networks and TRMM-3B42 version 7 rainfall dataset.” J. Hydrol. Eng., C4014007.
Lafdani, E. K., Nia, A. M., and Ahmadi, A. (2013). “Daily suspended sediment load prediction using artificial neural networks and support vector machines.” J. Hydrol., 478, 50–62.
Lamorski, K., Pachepsky, Y., Sławiński, C., and Walczak, R. T. (2008). “Using support vector machines to develop pedotransfer functions for water retention of soils in Poland.” Soil Sci. Soc. Am. J., 72(5), 1243–1247.
Licznar, P., and Nearing, M. (2003). “Artificial neural networks of soil erosion and runoff prediction at the plot scale.” Catena, 51(2), 89–114.
Lin, J. Y., Cheng, C. T., and Chau, K. W. (2006). “Using support vector machines for long-term discharge prediction.” Hydrol. Sci. J., 51(4), 599–612.
Liong, S. Y., and Sivapragasam, C. (2002). “Flood stage forecasting with support vector machines.” J. Am. Water Resour. Assoc., 38(1), 173–186.
Liu, Q. J., Shi, Z. H., Fang, N. F., Zhu, H. D., and Ai, L. (2013). “Modeling the daily suspended sediment concentration in a hyper-concentrated river on the Loess Plateau, China, using the wavelet-ANN approach.” Geomorphology, 186, 181–190.
Luo, H., Huang, Y., and Liu, D. (2013). “Prediction to chlorophyll-a concentration of impoundment process in Xiangxi Bay of Three Gorges Reservoir.” Conf. on Anthology, IEEE, New York, 1–3.
Maheswaran, R., and Khosa, R. (2012). “Comparative study of different wavelets for hydrologic forecasting.” Comput. Geosci., 46, 284–295.
Masters, T. (1993). Practical neural network recipes in C++, Academic Press, San Diego.
Mirbagheri, S. A., Tanji, K. K., and Krone, R. B. (1988). “Sediment characterization and transport in Colusa Basin Drain.” J. Environ. Eng., 1257–1273.
Misra, D., Oommen, T., Agarwal, A., Mishra, S. K., and Thompson, A. M. (2009). “Application and analysis of support vector machine based simulation for runoff and sediment yield.” Biosyst. Eng., 103(4), 527–535.
Muttil, N., and Lee, J. (2005). “Genetic programming for analysis and real-time prediction of coastal algal blooms.” Ecol. Model., 189(3–4), 363–376.
Pai, D. S., Sridhar, L., Badwaik, M. R., and Rajeevan, M. (2015). “Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution () gridded rainfall data set.” Climate Dyn., 45(3–4), 755–776.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and Mukhopadhyay, B. (2014). “Development of a new high spatial resolution () long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region.” Mausam, 65(1), 1–18.
Pandey, A., Himanshu, S. K., Mishra, S. K., and Singh, V. P. (2016). “Physically based soil erosion and sediment yield models revisited.” CATENA, 147, 595–620.
Partal, T., and Cigizoglu, H. K. (2008). “Estimation and forecasting of daily suspended sediment data using wavelet-neural networks.” J. Hydrol., 358(3), 317–331.
Qu, J., and Zuo, M. J. (2010). “Support vector machine based data processing algorithm for wear degree classification of slurry pump systems.” Measurement, 43(6), 781–791.
Rajaee, T. (2011). “Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers.” Sci. Total Environ., 409(15), 2917–2928.
Rajaee, T., Mirbagheri, S. A., Zounemat-Kermani, M., and Nourani, V. (2009). “Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.” Sci. Total Environ., 407(17), 4916–4927.
Rajaee, T., Nourani, V., Zounemat-Kermani, M., and Kisi, O. (2011). “River suspended sediment load prediction: Application of ANN and wavelet conjunction model.” J. Hydrol. Eng., 613–627.
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., 146–155.
Singh, V. P., Krstanovic, P. F., and Lane, L. J. (1998). “Stochastic models of sediment yield.” Modeling geomorphological systems, M. G. Anderson, ed., Wiley, Chichester, U.K., 272–286.
Sivakumar, B. (2006). “Suspended sediment load estimation and the problem of inadequate data sampling: A fractal view.” Earth Surf. Processes Landforms, 31(4), 414–427.
Sivapragasam, C., Liong, S. Y., and Pasha, M. F. K. (2001). “Rainfall and runoff forecasting with SSA-SVM approach.” J. Hydroinf., 3(3), 141–152.
Sivapragasam, C., Maheswaran, R., and Venkatesh, V. (2008). “Genetic programming approach for flood routing in natural channels.” Hydrol Process, 22(5), 623–628.
Sivapragasam, C., and Muttil, N. (2005). “Discharge rating curve extension—A new approach.” Water Resour. Manage., 19(5), 505–520.
Sudheer, C., Kumar, D., Prasad, R. K., and Mathur, S. (2013). “Optimal design of an in-situ bioremediation system using support vector machine and particle swarm optimization.” J. Contam. Hydrol., 151, 105–116.
Suryanarayana, C., Sudheer, C., Mahammood, V., and Panigrahi, B. K. (2014). “An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India.” Neurocomputing, 145, 324–335.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 232–239.
Tripathi, S., Srinivas, V. V., and Nanjundiah, R. S. (2006). “Downscaling of precipitation for climate change scenarios: A support vector machine approach.” J. Hydrol., 330(3), 621–640.
Twarakavi, N. K., Šimůnek, J., and Schaap, M. G. (2009). “Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines.” Soil Sci. Soc. Am. J., 73(5), 1443–1452.
Vapnik, V., and Chervonenkis, A. (1964). “A note on one class of perceptrons.” Autom. Remote. Control., 25(1), 103.
Vapnik, V., Golowich, S. E., and Smola, A. (1997). “Support vector method for function approximation, regression estimation, and signal processing.” Advances in neural information processing systems 9, M. Mozer, M. Jordan, and T. Petsche, eds., MIT Press, Cambridge, MA, 281–287.
Vapnik, V., and Lerner, A. (1963). “Pattern recognition using generalized portrait method.” Autom. Remote Control. 24(4), 774–780.
Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L. (2009). “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series.” J. Hydrol., 374(3), 294–306.
Wei, C. C. (2012). “Wavelet support vector machines for forecasting precipitation in tropical cyclones: Comparisons with GSVM, regression, and MM5.” Weather Forecasting, 27(2), 438–450.
White, S. (2005). “Sediment yield prediction and modeling.” Encyclopedia of hydrological sciences, Wiley, Chichester, U.K.
Yang, C. T. (1996). Sediment transport, theory and practice, McGraw-Hill, New York.
Yang, C. T., Marsooli, R., and Aalami, M. T. (2009). “Evaluation of total load sediment transport formulas using ANN.” Int. J. Sedim. Res., 24(3), 274–286.
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., and Lee, K. K. (2011). “A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer.” J. Hydrol., 396(1), 128–138.
Yu, P. S., Chen, S. T., and Chang, I. F. (2006). “Support vector regression for real-time flood stage forecasting.” J. Hydrol., 328(3), 704–716.
Yu, X., Liong, S. Y., and Babovic, V. (2004). “EC-SVM approach for real-time hydrologic forecasting.” J. Hydroinf., 6(3), 209–223.
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©2017 American Society of Civil Engineers.
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Received: May 3, 2016
Accepted: Dec 14, 2016
Published online: Mar 6, 2017
Published in print: Jul 1, 2017
Discussion open until: Aug 6, 2017
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