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98 results on '"Cominola, Andrea"'

1. Estimating irregular water demands with physics-informed machine learning to inform leakage detection

Author

Daniel, Ivo and Cominola, Andrea

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Leakages in drinking water distribution networks pose significant challenges to water utilities, leading to infrastructure failure, operational disruptions, environmental hazards, property damage, and economic losses. The timely identification and accurate localisation of such leakages is paramount for utilities to mitigate these unwanted effects. However, implementation of algorithms for leakage detection is limited in practice by requirements of either hydraulic models or large amounts of training data. Physics-informed machine learning can utilise hydraulic information thereby circumventing both limitations. In this work, we present a physics-informed machine learning algorithm that analyses pressure data and therefrom estimates unknown irregular water demands via a fully connected neural network, ultimately leveraging the Bernoulli equation and effectively linearising the leakage detection problem. Our algorithm is tested on data from the L-Town benchmark network, and results indicate a good capability for estimating most irregular demands, with R2 larger than 0.8. Identification results for leakages under the presence of irregular demands could be improved by a factor of 5.3 for abrupt leaks and a factor of 3.0 for incipient leaks when compared the results disregarding irregular demands., Comment: submitted to Water Research on July 17th, 2023

Published
2023

9. CaRDS - the statewide California Residential water Demand and Supply open dataset.

Author

Gross, Marie-Philine, Escriva-Bou, Alvar, Porse, Erik, and Cominola, Andrea

Subjects
WATER supply, WATER demand management, ENERGY demand management, WATER conservation, WATER shortages, WATER security, SUPPLY & demand
Abstract

As water scarcity becomes the new norm in the Western United States, states such as California have increased their efforts to improve water resilience. Achieving water security under climate change, population growth, and urbanization requires an integrated multi-sectoral approach, where adaptation strategies combine supply and demand management interventions. Yet, most studies consider supply-side and demand-side management strategies separately. Water conservation efforts are mainly driven by policy requirements and publicly available data to assess the effectiveness of demand- and supply-side management policies is often hard to find and unstructured. Here we present CaRDS - the statewide California Residential water Demand and Supply open dataset. CaRDS encompasses nine years (2013-2021) of monthly water supply and demand time series for 404 water suppliers in California, USA, compiled from different open-access data sources. Access to detailed temporal and spatial water supply operations and demands at the state-level can be useful to researchers and practitioners to realize applications such as evaluating the effectiveness of water conservation policies and discovering regional differences in water resilience measures. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Gamified Approaches for Water Management Systems: An Overview

Author

Castelletti, Andrea, primary, Cominola, Andrea, additional, Facchini, Alessandro, additional, Giuliani, Matteo, additional, Fraternali, Piero, additional, Herrera, Sergio, additional, Melenhorst, Mark, additional, Micheel, Isabel, additional, Novak, Jasminko, additional, Pasini, Chiara, additional, Rizzoli, Andrea-Emilio, additional, and Rottondi, Cristina, additional

Published
2018
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25. Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US.

Author

Veigel, Nadja, Kreibich, Heidi, and Cominola, Andrea

Subjects
MACHINE learning, FLOOD insurance, FLOOD risk, FLOODS, HELPING behavior, PHYSIOLOGICAL adaptation
Abstract

Floods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio‐economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio‐economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio‐economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities. Plain Language Summary: Flood resilience of individuals and communities can be improved by bottom‐up strategies, such as insurance purchase, or top‐down measures like the US National Flood Insurance Program's Community Rating System (CRS). Our interpretable machine learning approach shows that flood insurances are mostly purchased reactively, after the occurrence of a flood event. Yet, reactive behaviors are ill‐suited as more extreme events are expected under future climate, also in areas that were not previously flooded. The CRS counteracts this behavior by fostering proactive adaptation across a widespread range of socio‐economic backgrounds. Future risk management including the CRS should support and motivate individuals' proactive adaptation with a particular focus on highly vulnerable social groups to overcome existing inequalities in flood risk. Key Points: Flood insurance purchase in the US is dominated by reactive behavior after severe floodsThe Community Rating System (CRS) fosters proactive insurance adoption irrespective of socio‐economic backgroundThe CRS should further balance existing inequalities by targeting specific population segments [ABSTRACT FROM AUTHOR]

Published
2023
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34. ide3a: A Digital Campus for Transdisciplinary Research on Interconnected Urban Critical Infrastructure Systems

Author

Chen, Siling, primary, Hegyi, Dóra, additional, Huth, Margaux, additional, Brokhausen, Florian, additional, Türkcüoglu, Ongun, additional, Wiesner, Philipp, additional, Rabe, Jochen, additional, Park, Sangyoung, additional, Thamsen, Lauritz, additional, Hachaj, Paweł S., additional, Delauré, Yan, additional, Tscheikner-Gratl, Franz, additional, Castelletti, Andrea, additional, Beilharz, Jossekin, additional, Thamsen, Paul Uwe, additional, and Cominola, Andrea, additional

Published
2022
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35. ide3a: A Digital Campus for Transdisciplinary Research on Interconnected Urban Critical Infrastructure Systems

Author

Chen, Siling, primary, Hegyi, Dóra, additional, Huth, Margaux, additional, Brokhausen, Florian, additional, Türkcüoglu, Ongun, additional, Wiesner, Philipp, additional, Rabe, Jochen, additional, Park, Sangyoung, additional, Thamsen, Lauritz, additional, Hachaj, Paweł, additional, Delauré, Yan, additional, Tscheikner-Gratl, Franz, additional, Castelletti, Andrea, additional, Beilharz, Jossekin, additional, Thamsen, Paul, additional, and Cominola, Andrea, additional

Published
2021
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36. Assessing the Resilience of Water Distribution Networks Under Different Sensor Network Architectures and Data Sampling Frequencies

Author

Chen, Siling, Brokhausen, Florian, Wiesner, Philipp, Thamsen, Lauritz, and Cominola, Andrea

Abstract

Leakages and other infrastructure failures represent major challenges confronting the reliability, efficiency, and resilience of water distribution networks (WDNs). Timely identification and accurate localization of these anomalies is paramount to mitigate water and revenue losses, and avoid unwanted cascading effects. Previous resilience studies have demonstrated that the topology of WDNs affects their resilience, for instance to pipe failures. Moreover, several methodologies have been proposed in the last decades to identify optimal sensor locations in a WDN and monitor physical variables (e. g., pressure, flow, concentration of contaminants) in relation to specific objectives, including water contamination monitoring and leakage detection. Yet, most of the studies on optimal sensor placement in WDNs assume constant data logging frequency or number of available sensors, and do not comparatively analyze the influence of different sensor network architectures in relation to different WDN topologies. Here, we develop a simulation-based approach for WDN resilience assessment to quantify the influence and trade-offs of different sensor network architectures, data sampling frequencies, and WDN topologies on automatic leakage identification and localization capabilities.

Published
2021

40. A high-resolution pressure-driven method for leakage identification and localization in water distribution networks

Author

DANIEL, Ivo, PESANTEZ, Jorge, LETZGUS, Simon, KHAKSAR FASAEE, Mohammad Ali, ALGHAMDI, Faisal, MAHINTHAKUMAR, Kumar, BERGLUND, Emily, and COMINOLA, Andrea

Abstract

Water losses are one of the main consequences of infrastructure failures in water distribution networks (WDNs), accounting for more than 50% in some WDNs worldwide. Methods that support prompt detection and accurate localization of leakages are crucial to help water utilities implement timely mitigation measures and avoid unnecessary losses of water and revenues. This research develops a high-resolution pressure-driven method for leakage identification and localization in WDNs and tests it using the benchmark dataset provided as part of the "BattLeDIM", an international competition on leakage detection and localization. Our method is composed of two modules that operate sequentially. The first module performs leakage identification by processing pressure data observed at different sensor nodes in a WDN and by analyzing pressure differences between pairs of nodes. The model is trained using pressure data observed for a “normal” time period, i.e., without leak events occurring in the WDN. The reconstruction error from the model is then analyzed to detect the anomalies on a time series that potentially includes one or more simultaneous leak events. The leakage annotations from the leakage identification module are then used by the second module, which performs leakage localization. A simulation-optimization framework is adapted to locate leakages by iterative mixed-integer linear programming. The framework relies on the pressure response of a set of candidate pipes when a fixed leak value is inserted there. The candidate pipes are selected based on the pressure drop reported by the sensors, and candidate pipes are included in a search area located around the most affected sensor. Our proposed method is tested on the SCADA data provided with a 5-minute sampling frequency for the benchmark medium-sized WDN of L-Town. Preliminary experiments show that our pressure-driven method can promptly detect and localize most of the labeled leakages and it is suitable for real-time applications.

Published
2020
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