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Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation

Author

Listed:
  • Kichul Jung

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

  • Deg-Hyo Bae

    (Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea)

  • Myoung-Jin Um

    (Department of Civil Engineering, Kyonggi University, Suwon 16227, Korea)

  • Siyeon Kim

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

  • Seol Jeon

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

  • Daeryong Park

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

Abstract

The present work aimed to examine the feasibility of using artificial neural network (ANN) based models to obtain accurate estimates of nitrate loads in river basins, which is an important parameter for water quality management. Both Single ANN (SANN) and Ensemble ANN (EANN) models were used to obtain the load estimations for five river basins in the Midwest United States. These basins included the Cuyahoga, Raisin, Sandusky, Muskingum, and Vermilion basins in Michigan and Ohio. Further, canonical correlation analysis (CCA) was applied to the ANN models to improve the performance. The k-fold cross-validation method was then utilized to evaluate the proposed models based on two statistical indices, namely, the rRMSE and rBAIS , and the estimates were compared for four different k values (k = 3, 5, 7, and 10). According to the results, the EANN model seemed to produce better load estimations than the SANN model, and the CCA based EANN model tended to produce the best estimates among all of the proposed models in this study. The box plot data for the rRMSE index were also investigated, and the plot results indicated that increasing values of k tended to generate better estimates. Thus, the use of k = 10 is recommended for load estimations since this value was associated with better performances and less biased estimates.

Suggested Citation

  • Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:1:p:400-:d:305064
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    References listed on IDEAS

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    1. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    2. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
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    Cited by:

    1. Kichul Jung & Myoung-Jin Um & Momcilo Markus & Daeryong Park, 2020. "Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    2. Kichul Jung & Daeryong Park & Sangki Park, 2020. "Development of Models for Prompt Responses from Natural Disasters," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    3. Daeryong Park & Myoung-Jin Um & Momcilo Markus & Kichul Jung & Laura Keefer & Siddhartha Verma, 2021. "Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States," Sustainability, MDPI, vol. 13(13), pages 1-23, July.
    4. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).

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