Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting

Urban real-time rainfall-runoff forecasting (URRF) offers an economical and efficient approach to assessing flood risks in urban areas. However, the hydrological processes of urban rainfall are characterized by high nonlinearity and long-term dependencies due to strong uncertainties and significant...

Full description

Saved in:

Bibliographic Details
Published in:	Earth science informatics Vol. 18; no. 2; p. 403
Main Authors:	Tian, Yuan, Fu, Wenlong, Dong, Yajing, Han, Qi, Xiong, Qian, Cui, Ruonan, Ao, Zhenyu, Lei, Xinyu
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Subjects:	Ablation Artificial neural networks Deep learning Earth and Environmental Science Earth Sciences Earth System Sciences Encoders-Decoders Environmental risk Flood forecasting Flood management Flood risk Floods Human influences Hydrologic data Hydrologic processes Hydrology Information Systems Applications (incl.Internet) Machine learning Neural networks Ontology Rainfall runoff Rainfall-runoff relationships Real time Recurrent neural networks Risk management Runoff Runoff forecasting Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Time response Urban areas Urban rainfall Urban runoff Deep learning Global-encoder and local-decoder framework Temporal convolutional network Urban runoff Real-time prediction
ISSN:	1865-0473, 1865-0481
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!