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

K. S. Kasiviswanathan, Jianxun He (), Joo-Hwa Tay and K. P. Sudheer
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K. S. Kasiviswanathan: Indian Institute of Technology Mandi
Jianxun He: University of Calgary
Joo-Hwa Tay: University of Calgary
K. P. Sudheer: Indian Institute of Technology Madras

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 1, No 13, 229-243

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11269-018-2099-x

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