skip to main content
Tipo de recurso Mostra resultados com: Mostra resultados com: Índice

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

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Journal of hydrology (Amsterdam), December 2018, Vol.567, pp.743-758 [Periódico revisado por pares]

Texto completo disponível

Citações Citado por
  • Título:
    Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
  • Autor: Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan
  • Assuntos: Groundwater Levels ; Forecasting ; Neural Networks ; Narx ; Germany ; Groundwater Levels ; Forecasting ; Neural Networks ; Narx ; Germany ; Geography
  • É parte de: Journal of hydrology (Amsterdam), December 2018, Vol.567, pp.743-758
  • Descrição: •NARX were applied to obtain groundwater level forecasts with lead times up to half a year.•Porous, fractured and karst aquifers with and without external influences on groundwater levels.•The developed approach is easily transferable on other wells.•Input- and feedback delays were determined by applying STL time series decomposition.•The results indicate an outstanding suitability of NARX for groundwater level predictions. 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...
  • Idioma: Inglês

Buscando em bases de dados remotas. Favor aguardar.