Abstract
Models for solid transport in sewers during storm events are increasingly used. An important application of these models is the management of treatment plants during storm events so as to improve the quality of receiving waters. However, a major difficulty that prevents more general use of these tools is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. For that reason, a connectionist model called STORMNET has been designed to reproduce and replace usual conceptual and deterministic models. This model requires fewer data, can be automatically calibrated, and is comparatively simple. It is composed of two recurrent neural networks for the simulation of hydrographs and pollutographs of suspended solids, respectively. In this paper, we present an updated version of STORMNET designed for optimal management of wastewater treatment plants during storm events. This model has been validated using both model and real data. The results show the efficiency of STORMNET as a computational tool for simulating stormwater pollution.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
