Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical facto...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 14; S. 3678
Hauptverfasser:	Zhang, Wenting, Hu, Bin, Liu, Yongzhi, Zhang, Xingnan, Li, Zhixuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.07.2023
Schlagworte:	Artificial intelligence Atmospheric models China Climate change Comparative analysis decision support systems Decision trees Digital Elevation Models ensemble learning model Environmental risk Flood control Flood forecasting Flood management Flood mapping flood risk assessment Flooding Floods Hazardous areas Historic floods humans Learning algorithms Machine learning Multilayer perceptrons neural networks prediction Pumping stations Rain Remote sensing remote sensing data Resilience risk Risk assessment Rivers Stacking Support vector machines urban flood China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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