Statistical analyses of surface-water-quality variables in the coal area of southeastern Montana

1980 ◽  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Statistical Analyses ◽  
Water Quality Variables

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

2021 ◽  
Vol 13 (11) ◽  
pp. 6318
Author(s):  
Rafael Rodríguez ◽  
Marcos Pastorini ◽  
Lorena Etcheverry ◽  
Christian Chreties ◽  
Mónica Fossati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Missing Data ◽  
Surface Water ◽  
Water Resource ◽  
Surface Water Quality ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Variables ◽  
Monitoring Stations

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.

scholarly journals Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

2020 ◽  
Vol 12 (11) ◽  
pp. 4692 ◽  
Author(s):  
Angela Gorgoglione ◽  
Javier Gregorio ◽  
Agustín Ríos ◽  
Jimena Alonso ◽  
Christian Chreties ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Cluster Analysis ◽  
Surface Water ◽  
Land Cover ◽  
Winter Season ◽  
Surface Water Quality ◽  
Well Being ◽  
Land Use Land Cover ◽  
Water Quality Variables

Land use/land cover is one of the critical factors that affects surface-water quality at catchment scale. Effective mitigation strategies require an in-depth understanding of the leading causes of water pollution to improve community well-being and ecosystem health. The main aim of this study is to assess the relationship between land use/land cover and biophysical and chemical water-quality parameters in the Santa Lucía catchment (Uruguay, South America). The Santa Lucía river is the primary potable source of the country and, in the last few years, has had eutrophication issues. Several multivariate statistical analyses were adopted to accomplish the specific objectives of this study. The principal component analysis (PCA), coupled with k-means cluster analysis (CA), helped to identify a seasonal variation (fall/winter and spring/summer) of the water quality. The hierarchical cluster analysis (HCA) allowed one to classify the water-quality monitoring stations in three groups in the fall/winter season. The factor analysis (FA) with a rotation of the axis (varimax) was adopted to identify the most significant water-quality variables of the system (turbidity and flow). Finally, another PCA was run to link water-quality variables to the dominant land uses of the watershed. Strong correlations between TP and agriculture-land use, TP and livestock farming, NT and urban areas arose. It was found that these multivariate exploratory tools can provide a proper overview of the water-quality behavior in space and time and the correlations between water-quality variables and land use.

scholarly journals Water-Quality Data Imputation With High Percentage of Missing Values: A Machine Learning Approach

2021 ◽  
Author(s):  
Rafael Rodriguez ◽  
Marcos Pastorini ◽  
Lorena Etcheverry ◽  
Christian Chreties ◽  
Mónica Fossati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Data ◽  
Support Vector ◽  
Data Imputation ◽  
Water Quality Data ◽  
Water Quality Variables ◽  
Monitoring Stations

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in 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 implemented belonged to both 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 the ones positioned along the mainstream. IDW was the most chosen model for data imputation.

AN EVALUATION OF SUB-SURFACE WATER QUALITY AROUND BHARUCH-SURAT INDUSTRIAL REGION, GUJARAT, INDIA

2019 ◽  
Vol 38 (2) ◽  
pp. 200-220
Author(s):  
SOMNATH SAHA ◽  
◽  
SUKANTA KUMAR SAHA ◽  
TATHAGATA GHOSH ◽  
ROLEE KANCHAN ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Industrial Region

Surface Water Quality Assessment. Case Study: Târnava Mare River, Mediaș Town-Romania

2011 ◽  
Vol 4 (5) ◽  
pp. 70-72
Author(s):  
Cristina Roşu ◽  
◽  
Ioana Piştea ◽  
Carmen Roba ◽  
Mihaela Mihu ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Surface Water Quality ◽  
Water Quality Assessment
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