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Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling

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Abstract

This paper presents the construction of prediction intervals (PIs) associated with the Emotional Artificial Neural Network (EANN) via the Lower Upper Bound Estimation method (LUBE) and the classic Bootstrap method for the Suspended Sediment Load (SSL) modeling. As point prediction conveys no information about modeling reliability, PIs were applied. The constructed PIs via the EANN were also compared to those of the classic feed-forward Neural Network (FFNN) model. It was attempted to construct the PIs of the SSL modeling in both daily and monthly scales for two watersheds, Upper Rio Grande River, in the United States and the Lighvanchai River in Iran. The PIs were quantified via coverage and width criteria. In the LUBE method, Genetic Algorithm (GA) constructed the PIs by minimizing the cost function of Combinational coverage Width-based Criterion (CWC). Comparison of the results indicated that the criterion of the CWC for PIs of EANN was up to 72% and 78% lower than that to the PIs of the FFNN, respectively, in the LUBE and Bootstrap methods, which showed the reliability of the EANN. In addition, obtained results via the LUBE and the Bootstrap techniques denoted the lower level of uncertainty of the LUBE method. Comparing the CWC criterion of both methods indicated that for the LUBE method, CWC was up to 39% lower than that for the Bootstrap method. Also, the PIs of the FFNN for the Lighvanchai river modeling showed reliable results with CWC of 56% lower than Upper Rio Grande River, but PIs of the EANN led to lower level of uncertainty in Upper Rio Grande River modeling.

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Data availability

The applied data of the SSL were retrieved from Iran Water & Power Resources Development Co (IWPC) for the Lighvanchai station and from the United States Geological Survey’s website (USGS-https://cida.usgs.gov/sediment/) for the Rio Grande (at Otowi Bridge) station. The data used in the study are available from the corresponding author by request.

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Authors and Affiliations

  1. Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Elnaz Sharghi, Nardin Jabbarian Paknezhad & Hessam Najafi

  2. Faculty of Civil Engineering, University of Bonab, Bonab, Iran

    Hessam Najafi

Authors
  1. Elnaz Sharghi
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  2. Nardin Jabbarian Paknezhad
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  3. Hessam Najafi
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Correspondence to Elnaz Sharghi.

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All codes were provided via the Matlab software and are available.

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Communicated by: H. Babaie

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Sharghi, E., Paknezhad, N.J. & Najafi, H. Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling. Earth Sci Inform 14, 201–213 (2021). https://doi.org/10.1007/s12145-020-00567-1

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  • DOI: https://doi.org/10.1007/s12145-020-00567-1

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