Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 19
Articles | Volume 28, issue 19
https://doi.org/10.5194/hess-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 19
Research article
 | 
07 Oct 2024
Research article |  | 07 Oct 2024

Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features

Mariana Gomez, Maximilian Nölscher, Andreas Hartmann, and Stefan Broda

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Short summary
To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
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