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Data-based methods of flow forecasting are becoming increasingly popular due to their rapid development times, minimum information requirements, and ease of real-time implementation, with transfer fun...
This project is concerned with the comparison of two algorithms used in groundwater management models, based on Quadratic Programming (QP) and Non-linear Programming (NLP) models.A quadratic objective...
Rainfall runoff phenomena in small watersheds are favorably modelled using the kinematic wave approach. The present investigation considers excess rainfall as time dependent but ignores spatial effe...
Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear na...
Hysteresis is a rate-independent non-linearity that is expressed through thresholds, switches, and branches. Exceedance of a threshold, or the occurrence of a turning point in the input, switches the ...
A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural netw...

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