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Water Resources Management

, Volume 33, Issue 1, pp 229–243 | Cite as

Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling

  • K. S. Kasiviswanathan
  • Jianxun He
  • Joo-Hwa Tay
  • K. P. Sudheer
Article
  • 53 Downloads

Abstract

This paper presents a single-objective optimization-based perturbation analysis to quantify model prediction uncertainty. A new index named coverage width index (CWI), which combines two commonly used uncertainty indices, the percentage of coverage (POC) and the average width (AW), was proposed to facilitate the optimization. Considering the outperformance of the wavelet neural network (WNN) among various data-driven modeling approaches in hydrogeological modeling, the proposed approach was integrated into WNN (called OPWNN). A case study was conducted to demonstrate the application of OPWNN in groundwater level forecasting at two wells in the Amaravathi River Basin, India. The sensitivity analysis of the effect of initial perturbation range on CWI suggested that uncertainty is sensitive to the selected perturbation range and a small perturbation does not guarantee an acceptable prediction interval (PI). The modeling results demonstrated that the OPWNN can optimize the PI effectively with minimized AW corresponding to an expected high POC. Therefore, this approach can yield more reliable predictions/forecasts for water resources management.

Keywords

Hydrogeological modeling Model reliability Perturbation analysis Prediction interval Optimization Wavelet neural network 

Notes

Acknowledgements

The authors would like to thank the University of Calgary (Eyes High Program), Canada, for the financial support of this study. The data used in this study are available upon request from the corresponding author via email.

Compliance with Ethical Standards

Conflict of Interest

None.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • K. S. Kasiviswanathan
    • 1
    • 2
  • Jianxun He
    • 2
  • Joo-Hwa Tay
    • 2
  • K. P. Sudheer
    • 3
  1. 1.School of EngineeringIndian Institute of Technology MandiKamandIndia
  2. 2.Department of Civil Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  3. 3.Department of Civil EngineeringIndian Institute of Technology MadrasChennaiIndia

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