Articles | Volume 26, issue 15
Research article
05 Aug 2022
Research article |  | 05 Aug 2022

Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods

Marc Ohmer, Tanja Liesch, and Andreas Wunsch

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Cited articles

Alizadeh, Z. and Mahjouri, N.: A spatiotemporal Bayesian maximum entropy-based methodology for dealing with sparse data in revising groundwater quality monitoring networks: the Tehran region experience, Environ. Earth Sci., 76, 436,, 2017. a
Alizadeh, Z., Yazdi, J., and Moridi, A.: Development of an Entropy Method for Groundwater Quality Monitoring Network Design, Environ. Process., 5, 769–788,, 2018. a
Ammar, K., Khalil, A., McKee, M., and Kaluarachchi, J.: Bayesian deduction for redundancy detection in groundwater quality monitoring networks, Water Resour. Res., 44, W08412,, 2008. a
Annoni, J., Taylor, T., Bay, C., Johnson, K., Pao, L., Fleming, P., and Dykes, K.: Sparse-Sensor Placement for Wind Farm Control, J. Phys.: Conf. Ser., 1037, W08412,, 2018. a, b
Asefa, T., Kemblowski, M. W., Urroz, G., McKee, M., and Khalil, A.: Support vectors-based groundwater head observation networks design, Water Resour. Res., 40, W11509,, 2004. a
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.