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Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

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Listed:
  • Rafael Rodríguez

    (Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Marcos Pastorini

    (Instituto de Computación (InCo), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Lorena Etcheverry

    (Instituto de Computación (InCo), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Christian Chreties

    (Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Mónica Fossati

    (Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Alberto Castro

    (Instituto de Computación (InCo), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Angela Gorgoglione

    (Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

Abstract

The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.

Suggested Citation

  • Rafael Rodríguez & Marcos Pastorini & Lorena Etcheverry & Christian Chreties & Mónica Fossati & Alberto Castro & Angela Gorgoglione, 2021. "Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach," Sustainability, MDPI, vol. 13(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6318-:d:567774
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    References listed on IDEAS

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    1. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, September.
    2. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "k-Nearest Neighbor Classification," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 83-106, Springer.
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    1. 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.

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