Assessment of Suspended Sediment Load with Neural Networks in Arid Watershed


Here, the assessment of suspended sediment load is evaluated by four ANN algorithms: support vector machine (SVM), cascade-forward back-propagation (CFBP), feed-forward back-propagation (FFBP), and radial basis fewer neuron (RBFN) networks. Techniques are applied to a watershed of arid region, India. Sensitivity in terms of Nash–Sutcliffe coefficient (ENS), correlation coefficient (CC), and ratio between root mean square error and standard deviation (RSR) are computed. Results show that SVM shows preeminent value of RSR 0.0636, ENS 0.8869, and CC 0.9418, while Qt, Qt−1, Qt−2, Qt−3, Qt−4, Pt, Pt−1, Pt−2, Pt−3 architecture is applied. But for the same architecture, FFBP, RBFN and CFBP illustrate that the paramount value of CC is 0.9350, 0.9228, and 0.8985. As a whole, the performance of SVM shows superiority while considering various combinations of discharge and rainfall in contrast to FFBP, CFBP, and RBFN algorithm. Among all techniques, RBFN performs poor as compared to other algorithms. Interpretation of the results will help to compute sediment load in un-gauged catchments.

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  1. 1.

    A. Agarwal, R.D. Singh, S.K. Mishra, P.K. Bhunya, ANN based sediment yield models for Vamsadhara river basin (India). Water 31(1), 95–100 (2005)

    Google Scholar 

  2. 2.

    R. Arunkumar, V. Jothiprakash, K. Sharma, Artificial intelligence techniques for predicting and mapping daily pan evaporation. J. Inst. Eng. India Ser. A. 98, 219–231 (2017).

    Article  Google Scholar 

  3. 3.

    A. P. Atiken, Assessing systematic errors in rainfall-runoff models. J. Hydrol. 20(2), 121–136 (1973)

    Article  Google Scholar 

  4. 4.

    N. Bisoyi, H. Gupta, N.P. Padhy, G.J. Chakrapani, Prediction of daily sediment discharge using a back propagation neural network training algorithm: a case study of the Narmada River, India. Int. J. Sedim. Res. 34(2), 125–135 (2019)

    Article  Google Scholar 

  5. 5.

    K. Budu, Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting. J. Hydrol. Eng. 19(7), 1385–1400 (2014)

    Article  Google Scholar 

  6. 6.

    D.A.K. Fernando, A.W. Jayawardena, Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol. Eng. 3(3), 203–209 (1998)

    Article  Google Scholar 

  7. 7.

    O. Kisi, Multi-layer perceptrons with Levenberg Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol. Sci. J. 49(6), 1025–1040 (2004)

    Article  Google Scholar 

  8. 8.

    O. Kisi, Z.M. Yaseen, The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction. CATENA 174, 11–23 (2019)

    Article  Google Scholar 

  9. 9.

    K.C. Luk, J.E. Ball, A. Sharma, An application of artificial neural networks for rainfall forecasting. J. Math. Comput. Model 33, 683–693 (2001)

    Article  Google Scholar 

  10. 10.

    H. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15, 101–124 (2000)

    Article  Google Scholar 

  11. 11.

    W.S. Merritt, R.A. Letcher, A.J. Jakeman, A review of erosion and sediment transport models. Environ. Model. Softw. 18(8–9), 761–799 (2003)

    Article  Google Scholar 

  12. 12.

    A.W. Minns, M.J. Hall, Artificial neural networks as rainfall runoff models. Hydrol. Sci. J. 41(3), 399–417 (1996)

    Article  Google Scholar 

  13. 13.

    J. E. Nash, J. V. Sutcliffe, River flow forecasting through conceptual model, part 1: a discussion on principals. J. Hydrol. 10, 282–290 (1970)

    Article  Google Scholar 

  14. 14.

    V. Nourani, M.T. Alami, M.H. Aminfar, A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intel. 22(3), 466–472 (2009)

    Article  Google Scholar 

  15. 15.

    V. Nourani, M. Komasi, A. Mano, A multivariate ANN-wavelet approach for rainfall–runoff modelling. Water Resour. Manage. 23(14), 2877–2894 (2009)

    Article  Google Scholar 

  16. 16.

    V. Nourani, M. Komasi, M. Alami, Hybrid Wavelet-genetic programming approach to optimize ANN modeling of rainfall–runoff process. J. Hydrol. Eng. 403(6), 724–741 (2012)

    Article  Google Scholar 

  17. 17.

    L.S. Pereira, L.C. Andes, A.L. Cox, A. Ghulam, Measuring suspended-sediment concentration and turbidity in the middle Mississippi and Lower Missouri Rivers Using Landsat Data. JAWRA J. Am. Water Resour. Assoc. 54(2), 440–450 (2018)

    Article  Google Scholar 

  18. 18.

    M. Rahgoshay, S. Feiznia, M. Arian, S.A.A. Hashemi, Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm. Environ. Sci. Pollut. Res. 25(35), 35693–35706 (2018)

    Article  Google Scholar 

  19. 19.

    M. Rahgoshay, S. Feiznia, M. Arian, S.A.A. Hashemi, Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm. Arab. J. Geosci. 12(9), 277 (2019)

    Article  Google Scholar 

  20. 20.

    R.K. Rai, B.S. Mathur, Event-based sediment yield modelling using artificial neural network. Water Res. Manage. J. 22, 423–441 (2008)

    Article  Google Scholar 

  21. 21.

    T. Rajaee, V. Nourani, M. Zounemat-Kermani, O. Kisi, River suspended sediment load prediction: application of ANN and wavelet conjunction model. J. Hydrol. Eng. 16(8), 613–627 (2011)

    Article  Google Scholar 

  22. 22.

    K. Raza, V. Jothiprakash, Multi-output ANN model for prediction of seven meteorological parameters in a weather station. J. Inst. Eng. India Ser. A 95(4), 221–229 (2014).

    Article  Google Scholar 

  23. 23.

    M. Rezaeian Zadeh, S. Amin, D. Khalili, V.P. Singh, Daily outflow prediction by multi-layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour. Manag. 24(11), 2673–2688 (2010)

    Article  Google Scholar 

  24. 24.

    J.D. Salas, M. Markus, A.S. Tokar, Stream Flow Forecasting Based on Artificial Neural Networks (Kluwer Publishers, London, 2000), pp. 23–51

    Google Scholar 

  25. 25.

    S. Samantaray, D.K. Ghose, Evaluation of suspended sediment concentration using descent neural networks. Proc. Comput. Sci. 132, 1824–1831 (2018)

    Article  Google Scholar 

  26. 26.

    S. Samantaray, D.K. Ghose, Sediment assessment for a watershed in arid region via neural networks. Sadhana 44, 219 (2019)

    Article  Google Scholar 

  27. 27.

    S. Samantaray, A. Sahoo, Appraisal of runoff through BPNN, RNN, and RBFN in Tentulikhunti Watershed: a case study, in Frontiers in Intelligent Computing: Theory and Applications, vol. 1014, Advances in Intelligent Systems and Computing, ed. by S. Satapathy, V. Bhateja, B. Nguyen, N. Nguyen, D.N. Le (Springer, Singapore, 2020)

    Google Scholar 

  28. 28.

    S. Samantaray, A. Sahoo, Estimation of runoff through BPNN and SVM in Agalpur Watershed, in Frontiers in Intelligent Computing: Theory and Applications, vol. 1014, Advances in Intelligent Systems and Computing, ed. by S. Satapathy, V. Bhateja, B. Nguyen, N. Nguyen, D.N. Le (Springer, Singapore, 2020)

    Google Scholar 

  29. 29.

    S. Samantaray, A. Sahoo, Assessment of sediment concentration through RBNN and SVM-FFA in Arid Watershed, India, in Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol. 159, ed. by S. Satapathy, V. Bhateja, V. Bhateja, J. Mohanty, S. Udgata (Springer, Singapore, 2020)

    Google Scholar 

  30. 30.

    K. Samet, K. Hoseini, H. Karami, M. Mohammadi, Comparison between soft computing methods for prediction of sediment load in rivers: Maku dam case study. Iran. J. Sci. Technol. Trans. Civ. Eng. 43(1), 93–103 (2019)

    Article  Google Scholar 

  31. 31.

    V. Sharma, S.C. Negi, R.P. Rudra, S. Yang, Neural networks in predicting nitrate-nitrogen in drainage water. Agric. Water Manage. 63, 169–183 (2003)

    Article  Google Scholar 

  32. 32.

    M.B. Shukla, R. Kok, S.O. Prasher, G. Clark, R. Lacroix, Use of artificial neural networks in transient drainage design. Trans. ASAE 39, 119–124 (1996)

    Article  Google Scholar 

  33. 33.

    H. Torabi, R. Dehghani, Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran). Environ. Resour. Res. 6(2), 139–148 (2018)

    Google Scholar 

  34. 34.

    L. Yitian, R. R. Gu, Modeling flow and sediment transport in a river system using an artificial neural network. Environ. Manag. 31(1), 122–134 (2003)

    Article  Google Scholar 

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Correspondence to Dillip K. Ghose.

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Samantaray, S., Ghose, D.K. Assessment of Suspended Sediment Load with Neural Networks in Arid Watershed. J. Inst. Eng. India Ser. A 101, 371–380 (2020).

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  • Suspended sediment
  • SVM
  • FFBP
  • CFBP
  • RBFN
  • Nash–Sutcliffe coefficient
  • Arid watershed