Abstract
This contribution presents a Decision Support System for operators working in a wastewater treatment plant, aimed at helping them in taking appropriate mitigation strategies in case of extreme rainfall events. The Decision Support System is based on the real-time monitoring of several variables within the area of interest and on the forecasting of specific variables in key points. The forecasting relies on Artificial Neural Networks, predicting water levels and flows from rainfall inputs. The use of very-short-term Quantitative Precipitation Estimates – nowcasting – allows for an extension of the forecasting horizon with respect of using measured rainfall only. Different Artificial Neural Networks architectures are tested. The Decision Support System was developed and tested on a real setting, specifically a wastewater treatment plant collecting the sewage from the city of Brescia, Italy. The quickness of the computation is compliant with the real-time needs and makes the developed platform an efficient tool to be used in a Smart City.
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Acknowledgments
The study we developed in [10] is part of the INNOVA EFD3 research project financed by A2A Ciclo Idrico S.p.A. The additional research on the LSTM networks presented in this paper is part of the REACT project financed by Regione Veneto (IT) POR FESR 2014–2020 Asse I Azione 1.1.4.
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Termite, L.F. et al. (2022). A Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods. In: Klein, C., Jarke, M., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2021 2021. Communications in Computer and Information Science, vol 1612. Springer, Cham. https://doi.org/10.1007/978-3-031-17098-0_7
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