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

Wunsch, Andreas; Liesch, Tanja; Broda, Stefan

Abstract (englisch):
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. ... mehr

Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Poster
Publikationsdatum 15.12.2020
Sprache Englisch
Identifikator KITopen-ID: 1000128145
Veranstaltung 101. AGU Fall Meeting (2020), Online, 01.12.2020 – 17.12.2020
Bemerkung zur Veröffentlichung Die Veranstaltung fand wegen der Corona-Pandemie als Online-Event statt.
Schlagwörter Groundwater, Artificial Neural Networks, Artificial Intelligence, LSTM, CNN, NARX, Forecasting
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