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 Wünsch; Stefan Broda

doi:10.5194/hess-25-1671-2021

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 Wünsch(Fraunhofer Institute of Optronics, System Technologies and Image Exploitation), Stefan Broda(Federal Institute for Geosciences and Natural Resources)

Hydrology and earth system sciences

April 1, 2021

10.5194/hess-25-1671-2021

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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)

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