A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features
Abstract Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-...
Main Authors: | Hamed Farahmand, Yuanchang Xu, Ali Mostafavi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-04-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-023-32548-x |
Similar Items
-
Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting
by: Özlem Baydaroğlu, et al.
Published: (2024-03-01) -
A new verification approach for nowcasting based on intensity and spatial-temporal feature correction
by: Jun Liu, et al.
Published: (2024-12-01) -
Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
by: Alireza Jafari, et al.
Published: (2024-11-01) -
A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features
by: Jiajia Luo, et al.
Published: (2025-02-01) -
Leveraging LLMs for Financial News Analysis and Macroeconomic Indicator Nowcasting
by: Livia Reka Onozo, et al.
Published: (2024-01-01)