Simulation of urban wastewater systems using artificial neural networks: embedding urban areas in integrated catchment modelling

Guangtao Fu (corresponding author) David Butler Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, North Park Road, Harrison Building, Exeter EX4 4QF, UK Tel.:+44 1392 264075; Fax:+44 1392 217965; E-mail: [email protected] Christos Makropoulos Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, 5, Iroon Polytechniou Street, 157 80 Zografoy, Athens, Greece The urban wastewater system is an important part of integrated water management at the catchment level, yet, more often than not, inclusion of the system and its interaction with the surrounding catchment is either oversimplified or totally ignored in catchment modelling. Reasons of complexity and computational burden are mostly at the heart of this modelling gap. This paper proposes to use artificial neural networks (ANN) as a surrogate for the simulation of the urban wastewater system, allowing for a more realistic representation of the urban component to be incorporated into catchment models within a broad scale modelling framework. As a proof of concept, an integrated urban wastewater model is developed and its response in terms of both quantity and quality in combined sewer overflow (CSO) discharges and treatment plant effluent are captured and used to train a feedforward back-propagation ANN. The comparative results of the integrated urban water model and the ANN show good agreement for both water quantity and quality parameters. The resulting trained network is then embedded into a MIKE BASIN catchment model. It is suggested that ANN models greatly improve the level at which broad scale catchment models can accurately take into account urban–rural interactions.