Models for solid transport in sewers during storm events are increasingly used by engineers and operators to improve their systems and the quality of receiving waters. However, a major difficulty that prevents more general use of these models is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. Therefore, research has been carried out to assess the ability of connectionist models to reproduce and replace usual models for use by an operator. Such models require fewer data, are self-calibrated, and very easy to use. The first stage presented in this paper consists in a comparison between neural networks and the HYPOCRAS model, using simulations of real pollutographs for single storm events. Two specific recurrent neural networks based on the HYPOCRAS model and a general-purpose recurrent multilayer network are used to simulate hydrographs and pollutographs of TSS. The learning algorithm and the performance criterion used for optimization of these networks are described in detail. Experimental results with simulated and real data are then presented.
Neural networks for solid transport modelling in sewer systems during storm events
Ning Gong, Thierry Denoeux, Jean-Luc Bertrand-Krajewski; Neural networks for solid transport modelling in sewer systems during storm events. Water Sci Technol 1 April 1996; 33 (9): 85–92. doi: https://doi.org/10.2166/wst.1996.0183
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