An Artificial Neural Network-based Real Time DSS to Manage the Discharges of a Wastewater Treatment Plant and Reduce the Flooding Risk

Loris Termite, Emanuele Bonamente, Alberto Garinei, Alberto Garinei, Daniele Bolpagni, Lorenzo Menculini, Marcello Marconi, Marcello Marconi, Lorenzo Biondi, Lorenzo Biondi, Andrea Chini, Massimo Crespi

2021

Abstract

An approach for sewerage systems monitoring based on Artificial Neural Networks is presented as a feasible and reliable way of providing operators with a real-time Decision Support System that is able to predict critical events and suggest a proper mitigation strategy. A fully-working prototype was developed and tested on a sewerage system in the city of Brescia, Italy. The system is trained to forecast flows and water levels in critical points of the grid based on their measured values as well as rainfall data. When relying on observed rainfall only, key parameters can be predicted up to 60 minutes in advance, whereas including very-short-term Quantitative Precipitation Estimates – nowcasting – the time horizon can be extended further, up to 140 minutes in the current case study. Unlike classical hydraulic modelling, the proposed approach can be effectively used run-time as the execution is performed with a negligible computational cost, and it is suitable to increase safety measures in a Smart City context.

Paper Citation

in Harvard Style

Termite L., Bonamente E., Garinei A., Bolpagni D., Menculini L., Marconi M., Biondi L., Chini A. and Crespi M. (2021). An Artificial Neural Network-based Real Time DSS to Manage the Discharges of a Wastewater Treatment Plant and Reduce the Flooding Risk. In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-512-8, pages 15-26. DOI: 10.5220/0010396500150026

in Bibtex Style

@conference{smartgreens21,
author={Loris Termite and Emanuele Bonamente and Alberto Garinei and Daniele Bolpagni and Lorenzo Menculini and Marcello Marconi and Lorenzo Biondi and Andrea Chini and Massimo Crespi},
title={An Artificial Neural Network-based Real Time DSS to Manage the Discharges of a Wastewater Treatment Plant and Reduce the Flooding Risk},
booktitle={Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2021},
pages={15-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010396500150026},
isbn={978-989-758-512-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - An Artificial Neural Network-based Real Time DSS to Manage the Discharges of a Wastewater Treatment Plant and Reduce the Flooding Risk
SN - 978-989-758-512-8
AU - Termite L.
AU - Bonamente E.
AU - Garinei A.
AU - Bolpagni D.
AU - Menculini L.
AU - Marconi M.
AU - Biondi L.
AU - Chini A.
AU - Crespi M.
PY - 2021
SP - 15
EP - 26
DO - 10.5220/0010396500150026