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https://doi.org/10.5194/egusphere-2024-794
https://doi.org/10.5194/egusphere-2024-794
13 May 2024
 | 13 May 2024
Status: this preprint is open for discussion.

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier

Abstract. In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes.

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Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier

Status: open (until 08 Jul 2024)

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Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier
Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier

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Short summary
This study explores how deep learning can improve our understanding of groundwater levels, using an approach that combines climate data and physical characteristics of aquifers. By focusing on different types of groundwater levels and employing techniques like clustering and wavelet transform, the study highlights the importance of targeting relevant information. This research not only advances groundwater simulation but also emphasizes the benefits of different modelling approaches.