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Integrating Digital Twins and Machine Learning Framework for Smart and Sustainable Urban Water Management in Bolzano
Conference proceeding   Open access   Peer reviewed

Integrating Digital Twins and Machine Learning Framework for Smart and Sustainable Urban Water Management in Bolzano

C Slongo, SA Chungikar, E Niederwieser, D Siegele and Dominik Matt
Central Europe towards Sustainable Building 2025, Vol.1546, 012019
IOP Conference Series: Earth and Environmental Science, 1546
6th Central European Symposium on Building Physics - CESBP 2025 Budapest, Hungary (Budapest, 11/09/2025–13/09/2025)
2025
Handle:
https://hdl.handle.net/10863/51511

Abstract

This study presents the initial results of the ORCHESTRA project and offers initial indications that the integration of advanced digital technologies with Nature-based Solutions has the potential to significantly enhance urban water management in South Tyrol, with a focus on two pilot catchments in Bolzano. The proposed framework involves the development of a digital twin, which is continuously fed by dense IoT sensor networks, recreating the hydraulic behaviour of the city in real time. In parallel, a model calibrated by a Genetic Algorithm analyse these data to forecast runoff peaks and recommend adaptive control actions. The preliminary findings emphasise the potential of this hybrid strategy as a scalable solution for sustainable urban water management and that an interdisciplinary, data-driven approach can foster the transition toward intelligent, resilient and sustainable cities.
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Slongo_2025_IOP_Conf._Ser.__Earth_Environ._Sci._1546_012019721.79 kBDownloadView
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url
https://iopscience.iop.org/article/10.1088/1755-1315/1546/1/012019View

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