Articles | Volume 28, issue 9
https://doi.org/10.5194/hess-28-2167-2024
https://doi.org/10.5194/hess-28-2167-2024
Research article
 | 
17 May 2024
Research article |  | 17 May 2024

Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning

Andreas Wunsch, Tanja Liesch, and Nico Goldscheider

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Cited articles

Adamowski, J. and Chan, H. F.: A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28–40, https://doi.org/10.1016/j.jhydrol.2011.06.013, 2011. 
Arras, L., Osman, A., and Samek, W.: CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations, Inf. Fusion, 81, 14–40, https://doi.org/10.1016/j.inffus.2021.11.008, 2022. 
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W.: On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, PLOS ONE, 10, e0130140, https://doi.org/10.1371/journal.pone.0130140, 2015. 
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Short summary
Seasons have a strong influence on groundwater levels, but relationships are complex and partly unknown. Using data from wells in Germany and an explainable machine learning approach, we showed that summer precipitation is the key factor that controls the severeness of a low-water period in fall; high summer temperatures do not per se cause stronger decreases. Preceding winters have only a minor influence on such low-water periods in general.
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