Groundwater level forecasting with artificial neural networks:  a comparison of long short-term memory (LSTM),  convolutional neural networks (CNNs), and non-linear  autoregressive networks with exogenous input (NARX)

Wunsch, Andreas; Liesch, Tanja; Broda, Stefan

doi:https://doi.org/10.5194/hess-25-1671-2021

Articles | Volume 25, issue 3

https://doi.org/10.5194/hess-25-1671-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-1671-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 3

Research article

|

01 Apr 2021

Research article |

| 01 Apr 2021

Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Andreas Wunsch, Tanja Liesch, and Stefan Broda

Supplement

https://doi.org/10.5194/hess-25-1671-2021-supplement

Data sets

AndreasWunsch/Groundwater-Level-Forecasting-with-ANNs-A-Comparison-of-LSTM-CNN-and-NARX A. Wunsch https://doi.org/10.5281/zenodo.4018722

Model code and software

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