Rehm, Imke-Sophie ; Friesen, John ; Pouls, Kevin ; Busch, Christoph ; Taubenböck, Hannes ; Pelz, Peter F. (2023)
A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data.
In: Water, 2021, 13 (16)
doi: 10.26083/tuprints-00019608
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures.
Typ des Eintrags: | Artikel |
---|---|
Erschienen: | 2023 |
Autor(en): | Rehm, Imke-Sophie ; Friesen, John ; Pouls, Kevin ; Busch, Christoph ; Taubenböck, Hannes ; Pelz, Peter F. |
Art des Eintrags: | Zweitveröffentlichung |
Titel: | A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data |
Sprache: | Englisch |
Publikationsjahr: | 14 November 2023 |
Ort: | Darmstadt |
Publikationsdatum der Erstveröffentlichung: | 2021 |
Ort der Erstveröffentlichung: | Basel |
Verlag: | MDPI |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Water |
Jahrgang/Volume einer Zeitschrift: | 13 |
(Heft-)Nummer: | 16 |
Kollation: | 20 Seiten |
DOI: | 10.26083/tuprints-00019608 |
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19608 |
Zugehörige Links: | |
Herkunft: | Zweitveröffentlichung DeepGreen |
Kurzbeschreibung (Abstract): | Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures. |
Freie Schlagworte: | water distribution systems, water infrastructure generation, resilience, water demand specification, geo-referenced data, local climate zones, urban water demand |
Status: | Verlagsversion |
URN: | urn:nbn:de:tuda-tuprints-196085 |
Zusätzliche Informationen: | This article belongs to the Special Issue Resilience of Interdependent Urban Water Systems |
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau 600 Technik, Medizin, angewandte Wissenschaften > 624 Ingenieurbau und Umwelttechnik |
Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Fluidsystemtechnik (FST) (seit 01.10.2006) |
Hinterlegungsdatum: | 14 Nov 2023 13:57 |
Letzte Änderung: | 15 Nov 2023 08:34 |
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