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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features
Hamed Farahmand, Yuanchang Xu, Ali Mostafavi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 36, Iss. 10, pp. 5388-5408
Open Access | Times Cited: 143
Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism
Yu Shao, Jiarui Chen, Tuqiao Zhang, et al.
Journal of Hydroinformatics (2024) Vol. 26, Iss. 6, pp. 1409-1424
Open Access | Times Cited: 8
Graph Neural Network for Spatiotemporal Data: Methods and Applications
Yun Li, Dazhou Yu, Zhenke Liu, et al.
(2024)
Open Access | Times Cited: 6
Artificial Intelligence Algorithms in Flood Prediction: A General Overview
Manish Pandey
Springer eBooks (2024), pp. 243-296
Closed Access | Times Cited: 5
Improving flood forecasting using time-distributed CNN-LSTM model: a time-distributed spatiotemporal method
Haider Ali Malik, Jun Feng, Pingping Shao, et al.
Earth Science Informatics (2024)
Closed Access | Times Cited: 5
Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada
Paulo Alexandre Costa Rocha, Victor Oliveira Santos, John Scott, et al.
International Journal of River Basin Management (2024), pp. 1-18
Closed Access | Times Cited: 4
Predicting real-time roadway pluvial flood risk: A hybrid machine learning approach coupling a graph-based flood spreading model, historical vulnerabilities, and Waze data
Arefeh Safaei-moghadam, Azadeh Hosseinzadeh, Barbara Minsker
Journal of Hydrology (2024) Vol. 637, pp. 131406-131406
Open Access | Times Cited: 4
Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network
M. Rösch, Michael Nolde, Tobias Ullmann, et al.
Fire (2024) Vol. 7, Iss. 6, pp. 207-207
Open Access | Times Cited: 4
Artificial Intelligence for Flood Risk Management: A Comprehensive State-of-the-Art Review and Future Directions
Zhewei Liu, Natalie Coleman, Flavia Ioana Patrascu, et al.
International Journal of Disaster Risk Reduction (2024) Vol. 117, pp. 105110-105110
Closed Access | Times Cited: 4
Artificial Intelligence for Flood Risk Management: A Comprehensive State-of-the-Art Review and Future Directions
Zhewei Liu, Natalie Coleman, Flavia Ioana Patrascu, et al.
(2025)
Closed Access
A spatiotemporal watershed-scale machine-learning model for hourly and daily flood-water level prediction: the case of the tidal Beigang River, Taiwan
Wen‐Dar Guo, Wei‐Bo Chen, Chih-Hsin Chang
Natural Hazards (2025)
Closed Access
Estimation of unplanned water use based on system dynamics model in arid areas
Wang Xin, Minghong Tan, Xingyuan Xiao
Agricultural Water Management (2025) Vol. 312, pp. 109448-109448
Open Access
Analyzing Common Social and Physical Features of Flash-Flood Vulnerability in Urban Areas
Natalie Coleman, Allison Clarke, Miguel Esparza, et al.
International Journal of Disaster Risk Reduction (2025), pp. 105437-105437
Closed Access
U-RNN high-resolution spatiotemporal nowcasting of urban flooding
Xiaoyan Cao, Bao-Ying Wang, Yao Yao, et al.
Journal of Hydrology (2025), pp. 133117-133117
Closed Access
An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction
Fei Ding, Shilong Hao, Mingcen Jiang, et al.
Ecological Informatics (2025), pp. 103126-103126
Open Access
A Cluster-based Temporal Attention Approach for Predicting Cyclone-induced Compound Flood Dynamics
Samuel Daramola, David F. Muñoz, Hamed Moftakhari, et al.
Environmental Modelling & Software (2025), pp. 106499-106499
Closed Access
Flooddamagecast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation
Chia-Fu Liu, Lipai Huang, Kai Yin, et al.
(2024)
Open Access | Times Cited: 3
Construction of user-adaptive urban waterlogging emergency scenarios considering mapping concerns
Shuai Hong, Ziyu Liu, Jie Shen, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 131, pp. 103953-103953
Open Access | Times Cited: 2
Deep Learning Ensemble for Flood Probability Analysis
Fred Sseguya, Kyung Soo Jun
Water (2024) Vol. 16, Iss. 21, pp. 3092-3092
Open Access | Times Cited: 2
A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems
Jiayu Ding, Yuewei Wang, Chaoyue Li
Land (2024) Vol. 13, Iss. 6, pp. 753-753
Open Access | Times Cited: 1
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
Zhiyu Zhang, Wenchong Tian, Chenkaixiang Lu, et al.
Water Research (2024) Vol. 263, pp. 122142-122142
Open Access | Times Cited: 1
Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling
Yogesh Bhattarai, Sunil Bista, Rocky Talchabhadel, et al.
Total Environment Advances (2024) Vol. 12, pp. 200116-200116
Closed Access | Times Cited: 1
Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach
Ashlin Ann Alexander, D. Nagesh Kumar
Advances in Water Resources (2024) Vol. 194, pp. 104842-104842
Closed Access | Times Cited: 1
Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks
Muhammed Sit, Bekir Zahit Demiray, İbrahim Demir
Research Square (Research Square) (2023)
Open Access | Times Cited: 3
Reducing the Computational Cost of Urban Flood Prediction in Los Angeles
Laura Bedoyan, Mansoureh Lord, Adam Kaplan
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (2024), pp. 0498-0506
Closed Access
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