Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area
As groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/1/148 |
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| author | Chiara Zanotti Marco Rotiroti Agnese Redaelli Mariachiara Caschetto Letizia Fumagalli Camilla Stano Davide Sartirana Tullia Bonomi |
| author_facet | Chiara Zanotti Marco Rotiroti Agnese Redaelli Mariachiara Caschetto Letizia Fumagalli Camilla Stano Davide Sartirana Tullia Bonomi |
| author_sort | Chiara Zanotti |
| collection | DOAJ |
| description | As groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis and time series cluster analysis are applied to groundwater contamination data with the aim of developing data-driven monitoring strategies. The study area is an urban area characterized by several superimposing historical contamination sources and a complex hydrogeological setting. A multivariate time series cluster analysis was performed on PCE and TCE concentrations data over a 10 years time span. The time series clustering was performed based on the Dynamic Time Warping method. The results of the clustering identified 3 clusters associated with diffuse background contamination and 7 clusters associated with local hotspots, characterized by specific time profiles. Similarly, a univariate time series cluster analysis was applied to Cr(VI) data, identifying 3 background clusters and 7 hotspots, including 4 singletons. The clustering outputs provided the basis for the implementation of data-driven monitoring strategies and early warning systems. For the clusters associated with diffuse background contaminations and those with constant trends, trigger levels were calculated with the 95° percentile, constituting future threshold values for early warnings. For the clusters with pluriannual trends, either oscillatory or monotonous, specific monitoring strategies were proposed based on trends’ directions. Results show that the spatio-temporal overview of the data variability obtained from the time series cluster analysis helped to extract relevant information from the data while neglecting measurements noise and uncertainty, supporting the implementation of a more efficient groundwater quality monitoring. |
| first_indexed | 2024-03-09T09:38:00Z |
| format | Article |
| id | doaj.art-a1434444d8044622ad158f95e36e18f0 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T09:38:00Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a1434444d8044622ad158f95e36e18f02023-12-02T01:15:57ZengMDPI AGWater2073-44412022-12-0115114810.3390/w15010148Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban AreaChiara Zanotti0Marco Rotiroti1Agnese Redaelli2Mariachiara Caschetto3Letizia Fumagalli4Camilla Stano5Davide Sartirana6Tullia Bonomi7Department of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyA2A Ciclo Idrico S.p.A, 25124 Brescia, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyDepartment of Earth and Environmental Sciences, University of Milano—Bicocca, 20126 Milan, ItalyAs groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis and time series cluster analysis are applied to groundwater contamination data with the aim of developing data-driven monitoring strategies. The study area is an urban area characterized by several superimposing historical contamination sources and a complex hydrogeological setting. A multivariate time series cluster analysis was performed on PCE and TCE concentrations data over a 10 years time span. The time series clustering was performed based on the Dynamic Time Warping method. The results of the clustering identified 3 clusters associated with diffuse background contamination and 7 clusters associated with local hotspots, characterized by specific time profiles. Similarly, a univariate time series cluster analysis was applied to Cr(VI) data, identifying 3 background clusters and 7 hotspots, including 4 singletons. The clustering outputs provided the basis for the implementation of data-driven monitoring strategies and early warning systems. For the clusters associated with diffuse background contaminations and those with constant trends, trigger levels were calculated with the 95° percentile, constituting future threshold values for early warnings. For the clusters with pluriannual trends, either oscillatory or monotonous, specific monitoring strategies were proposed based on trends’ directions. Results show that the spatio-temporal overview of the data variability obtained from the time series cluster analysis helped to extract relevant information from the data while neglecting measurements noise and uncertainty, supporting the implementation of a more efficient groundwater quality monitoring.https://www.mdpi.com/2073-4441/15/1/148groundwater contaminationdiffuse contaminationdynamic time warpinganthropic background leveltime series analysisearly warning system |
| spellingShingle | Chiara Zanotti Marco Rotiroti Agnese Redaelli Mariachiara Caschetto Letizia Fumagalli Camilla Stano Davide Sartirana Tullia Bonomi Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area Water groundwater contamination diffuse contamination dynamic time warping anthropic background level time series analysis early warning system |
| title | Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area |
| title_full | Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area |
| title_fullStr | Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area |
| title_full_unstemmed | Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area |
| title_short | Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area |
| title_sort | multivariate time series clustering of groundwater quality data to develop data driven monitoring strategies in a historically contaminated urban area |
| topic | groundwater contamination diffuse contamination dynamic time warping anthropic background level time series analysis early warning system |
| url | https://www.mdpi.com/2073-4441/15/1/148 |
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