Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany ; day:03 ; month:11 ; year:2022 ; pages:1-23 ; extent:23
Natural hazards and earth system sciences discussions ; (03.11.2022), 1-23 (gesamt 23)
- DOI
-
10.5194/nhess-2022-263
- URN
-
urn:nbn:de:101:1-2022111004345027948501
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:25 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Other Objects (12)
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
Exploring the transferability of flood loss models across flood types
Adaptability and transferability of flood loss functions in residential areas
Data-driven Flood Analysis and Decision Support
A Contribution on the Transferability of Data-Driven Models for Bearing Fault Diagnosis
Machine-learning to predict anharmonic frequencies: a study of models and transferability †
Transferability of machine learning-based modeling frameworks across flood events for hindcasting maximum river flood depths in coastal watersheds
Towards urban pluvial flood mapping using data-driven models
Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises
PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS
Transferability of integration models
Data-driven modeling of areas prone to heavy rain-induced floods
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
Exploring the transferability of flood loss models across flood types
Adaptability and transferability of flood loss functions in residential areas
Data-driven Flood Analysis and Decision Support
A Contribution on the Transferability of Data-Driven Models for Bearing Fault Diagnosis
Machine-learning to predict anharmonic frequencies: a study of models and transferability †
Transferability of machine learning-based modeling frameworks across flood events for hindcasting maximum river flood depths in coastal watersheds
Towards urban pluvial flood mapping using data-driven models
Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises
PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS
Transferability of integration models
Data-driven modeling of areas prone to heavy rain-induced floods
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
Exploring the transferability of flood loss models across flood types
Adaptability and transferability of flood loss functions in residential areas
Data-driven Flood Analysis and Decision Support
A Contribution on the Transferability of Data-Driven Models for Bearing Fault Diagnosis
Machine-learning to predict anharmonic frequencies: a study of models and transferability †
Transferability of machine learning-based modeling frameworks across flood events for hindcasting maximum river flood depths in coastal watersheds
Towards urban pluvial flood mapping using data-driven models
Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises
PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS
Transferability of integration models