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Wasser und Abfall

Deep Learning zur Optimierung der Trinkwasserversorgung aus Talsperren

  • 01-05-2025
  • Wasser
Published in:
Authors
Gregor Johnen
Prof. Dr.-Ing. André Niemann
Patrick Nistahl
Dr. Alexander Hutwalker
Published in
Wasser und Abfall | Issue 5/2025

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Excerpt

Vielschichtige neuronale Netzwerke, auch bekannt als Deep-Learning-Modelle, ermöglichen präzise und schnelle Vorhersagen in der Wasserwirtschaft, insbesondere des Wasserbedarfs oder auch des Gebietsabflusses. Gleichzeitig bieten sie flexible Möglichkeiten zur Ableitung der Prognoseunsicherheit, welche einen essenziellen Bestandteil der risikobasierten Entscheidungsfindung darstellt, so kann die Wasserversorgung aus Talsperren effizienter, widerstandsfähiger und anpassungsfähiger gestaltet werden. …

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previous article Erratum zu: WASSER UND ABFALL 11/2024, „Stark unterschiedliche Starkregengefahrenkarten nach den Leitfäden der Länder“ auf den Seiten 24 ff.
next article Digitale Zwillinge von Smart Forestry zu Smart Water Management
Title
Deep Learning zur Optimierung der Trinkwasserversorgung aus Talsperren
Authors
Gregor Johnen
Prof. Dr.-Ing. André Niemann
Patrick Nistahl
Dr. Alexander Hutwalker
Publication date
01-05-2025
Publisher
Springer Fachmedien Wiesbaden
Published in
Wasser und Abfall / Issue 5/2025
Print ISSN: 1436-9095
Electronic ISSN: 2192-8754
DOI
https://doi.org/10.1007/s35152-025-1982-z
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