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.

Zanotti, C., Rotiroti, M., Redaelli, A., Caschetto, M., Fumagalli, L., Stano, C., et al. (2023). Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area. WATER, 15(1) [10.3390/w15010148].

Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area

Zanotti, Chiara
Primo
;
Rotiroti, Marco
Secondo
;
Redaelli, Agnese;Caschetto, Mariachiara;Fumagalli, Letizia;Sartirana, Davide;Bonomi, Tullia
Ultimo
2023

Abstract

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.
Articolo in rivista - Articolo scientifico
anthropic background level; diffuse contamination; dynamic time warping; early warning system; groundwater contamination; time series analysis;
English
30-dic-2022
2023
15
1
148
open
Zanotti, C., Rotiroti, M., Redaelli, A., Caschetto, M., Fumagalli, L., Stano, C., et al. (2023). Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area. WATER, 15(1) [10.3390/w15010148].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/400796
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