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• W97331552 abstract "A variety of different processes is known that determine water and solute fluxes in headwater catchments. Water resources management of these systems, however, relies in most cases on empirical experience with respect to its overall response. A promising method to bridge the gap between comprehensive scientific investigations and the need to manage the systems on the basis of limited data sets seems to be the application of artificial neural networks (ANN). Here, time series of NO 3 - concentrations in the runoff of two forested headwater catchments in south Germany are investigated. Furthermore, the application of nonlinear methods presented here reveals a rather intricate behaviour also on the temporal scale, and considerable differences between the two catchments. This demonstrates the validity of ANN as universal descriptive tools." @default.
• W97331552 created "2016-06-24" @default.
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• W97331552 date "1998-01-01" @default.
• W97331552 modified "2023-09-26" @default.
• W97331552 title "Neural Network Modelling of NO3-Time Series from small Headwater Catchments" @default.
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