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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)

Wunsch, Andreas; Liesch, Tanja; Broda Stefan

It is now well established to use shallow artificial neural networks (ANNs) 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, shallow recurrent networks frequently seem to be excluded from the study design due to 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 ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both 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

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Verlagsausgabe §
DOI: 10.5445/IR/1000131394
Veröffentlicht am 16.04.2021
DOI: 10.5194/hess-25-1671-2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1027-5606, 1607-7938
KITopen-ID: 1000131394
Erschienen in Hydrology and Earth System Sciences
Verlag Copernicus Publications
Band 25
Heft 3
Seiten 1671-1687
Nachgewiesen in Scopus
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