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

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 an...

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Bibliographic Details
Published in:Sustainability 2021-06, Vol.13 (11), p.6318
Main Authors: Rodríguez, Rafael, Pastorini, Marcos, Etcheverry, Lorena, Chreties, Christian, Fossati, Mónica, Castro, Alberto, Gorgoglione, Angela
Format: Article
Language:English
Subjects:
Bayesian analysis
Datasets
Developing countries
LDCs
Learning algorithms
Machine learning
Mathematical models
Missing data
Monitoring
Neural networks
Nitrogen
Precipitation
Resource management
Rivers
Statistical analysis
Stream flow
Surface water
Sustainability
Time series
Variables
Water management
Water quality
Water resources management
Water temperature
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13116318