A Comparative Analysis of Artificial Neural Network and Support Vector Regression for River Suspended Sediment Load Prediction

  • Barenya Bikash Hazarika
  • Deepak GuptaEmail author
  • Ashu
  • Mohanadhas Berlin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)


The artificial neural network (ANN) model and support vector regression (SVR) model have gained tremendous popularity among the researchers during the past couple of decades. Both of the models are very powerful in prediction and have several applications in different fields, which also include suspended sediment load prediction. In this work, the predictive capability of ANN and SVR model is investigated to estimate the daily suspended sediment load (SSL) in Tawang Chu River, Jang of Arunachal Pradesh, India. The performance of the models is evaluated using three quality measuring parameters, i.e., mean squared error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE). From the experimental results, one can conclude that the predictive capability of SVR is better compared to ANN in terms of all of the quality measuring parameters.


Artificial neural network Support vector regression Suspended sediment load Prediction 



This work was fully supported by the Science and Engineering Research Board, Government of India (SERB), under early career research award ECR/2016/001464. We are also thankful to NHPC LIMITED, Tawang Basin Project to provide this data.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Barenya Bikash Hazarika
    • 1
  • Deepak Gupta
    • 1
    Email author
  • Ashu
    • 1
  • Mohanadhas Berlin
    • 1
  1. 1.National Institute of Technology Arunachal PradeshYupiaIndia

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