Conjunto de datos de: Water-quality data imputation with a high percentage of missing values: a machine learning approach

To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases.

In this dataset, we include the original and imputed values for the following variables: - Water temperature (Tw) - Dissolved oxygen (DO) - Electrical conductivity (EC) - pH - Turbidity (Turb) - Nitrite (NO2-) - Nitrate (NO3-) - Total Nitrogen (TN) Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC]. More details about the study area, the original datasets, and the methodology adopted can be found in our paper mdpi.com/2071-1050/13/11/6318.

If you use this dataset in your work, please cite our paper: Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. doi.org/10.3390/su13116318