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Quantification of Prediction Uncertainty in Artificial Neural Network Models

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

The research towards improving the prediction and forecasting of artificial neural network (ANN) based models has gained significant interest while solving various engineering problems. Consequently, different approaches for the development of ANN models have been proposed. However, the point estimation of ANN forecasts seldom explains the actual mechanism that brings the relationship among modeled variables. This raises the question on the model output while making decisions due to the inherent variability or uncertainty associated. The standard procedure though available for the quantification of uncertainty, their applications in ANN model are still limited. In this chapter, commonly employed uncertainty methods such as bootstrap and Bayesian are applied in ANN and demonstrated through a case example of flood forecasting models. It also discusses the merits and limitations of bootstrap ANN (BTANN) and Bayesian ANN (BANN) models in terms of convergence of parameter and quality of prediction interval evaluated using uncertainty indices.

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Authors and Affiliations

  1. Department of Civil Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada

    K. S. Kasiviswanathan & Jianxun He

  2. Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    K. P. Sudheer

Authors
  1. K. S. Kasiviswanathan
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  2. K. P. Sudheer
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  3. Jianxun He
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Corresponding author

Correspondence to K. P. Sudheer .

Editor information

Editors and Affiliations

  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Kasiviswanathan, K.S., Sudheer, K.P., He, J. (2016). Quantification of Prediction Uncertainty in Artificial Neural Network Models. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-28495-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28493-4

  • Online ISBN: 978-3-319-28495-8

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