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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-552
https://doi.org/10.5194/hess-2020-552

  23 Nov 2020

23 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal HESS.

Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX

Andreas Wunsch1, Tanja Liesch1, and Stefan Broda2 Andreas Wunsch et al.
  • 1Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Division of Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
  • 2Federal Institute for Geosciences and Natural Resources (BGR), Wilhelmstr. 25-30, 13593 Berlin, Germany

Abstract. It is now well established to use shallow artificial neural networks (ANN) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, especially shallow recurrent networks frequently seem to be excluded from the study design despite the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANN namely nonlinear autoregressive networks with exogenous inputs (NARX), and popular state-of-the-art DL-techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN). We compare both the performance on sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period, while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. We observe that for seq2val forecasts NARX models on average perform best, however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL-models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL-techniques; however, LSTMs and CNNs might perform substantially better with a larger data set, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

Andreas Wunsch et al.

 
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Andreas Wunsch et al.

Model code and software

Groundwater-Level-Forecasting-with-ANNs-A-Comparison-of-LSTM-CNN-and-NARX: v1.1 Andreas Wunsch, Tanja Liesch, and Stefan Broda https://doi.org/10.5281/zenodo.4121854

Andreas Wunsch et al.

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