Articles | Volume 26, issue 15
https://doi.org/10.5194/hess-26-4033-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4033-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2022
Research article |
| 05 Aug 2022
| 05 Aug 2022
Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Tanja Liesch
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Andreas Wunsch
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Short summary
We present a data-driven approach to select optimal locations for groundwater monitoring wells. The applied approach can optimize the number of wells and their location for a network reduction (by ranking wells in order of their information content and reducing redundant) and extension (finding sites with great information gain) or both. It allows us to include a cost function to account for more/less suitable areas for new wells and can help to obtain maximum information content for a budget.
We present a data-driven approach to select optimal locations for groundwater monitoring wells....
