Assessment of Suspended Sediment Load with Neural Networks in Arid Watershed

Abstract

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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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). https://doi.org/10.1007/s40030-019-00429-0

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Keywords

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