Smart City Gnosys
Smart city article details
| Title | An Artificial Neural Network-Based Real Time Dss To Manage The Discharges Of A Wastewater Treatment Plant And Reduce The Flooding Risk |
|---|---|
| ID_Doc | 7648 |
| Authors | Termite L.F.; Bonamente E.; Garinei A.; Bolpagni D.; Menculini L.; Marconi M.; Biondi L.; Chini A.; Crespi M. |
| Year | 2021 |
| Published | International Conference on Smart Cities and Green ICT Systems, SMARTGREENS - Proceedings, 2021-April |
| DOI | http://dx.doi.org/10.5220/0010396500150026 |
| 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. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. |
| Author Keywords | Artificial Neural Networks; Decision Support System; Flood Forecasting; Flood Management; Smart Infrastructures |
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