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Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)

Wunsch, Andreas; Liesch, Tanja; Broda, Stefan

Abstract (englisch):
While the application of neural networks for groundwater level forecasting in general has been investigated by many authors, the use of nonlinear autoregressive networks with exogenous inputs (NARX) is relatively new. For this work NARX were applied to obtain groundwater level forecasts for several wells in southwest Germany. Wells in porous, fractured and karst aquifers were investigated and forecasts of lead times up to half a year were conducted for both influenced (e.g. nearby pumping) and uninfluenced wells. Precipitation and temperature were chosen as predictors, which makes the selected approach easily transferable, since both parameters are widely available and simple to measure. Input and feedback delays were determined by applying STL time series decomposition on the data and using auto- and cross-correlation functions on the remainders to determine significant time lags. Coefficient of determination, (relative) root mean squared error and Nash-Sutcliffe efficiency were used to evaluate forecasts, the model selection was based on an out-of-sample validation on rolling basis. The results are promising and indicate an outstanding suitability of NARX for groundwater level predictions with such a small set of inputs in all three aquifer types.

DOI: 10.1016/J.JHYDROL.2018.01.045
Zitationen: 50
Web of Science
Zitationen: 38
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2018
Sprache Englisch
Identifikator ISSN: 0022-1694
KITopen-ID: 1000094735
Erschienen in Journal of hydrology
Verlag Elsevier
Band 567
Seiten 743–758
Schlagwörter Groundwater levels, Forecasting, Neural networks, NARX, Germany
Nachgewiesen in Scopus
Web of Science
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