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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 quantile-based encoder-decoder framework for multi-step ahead runoff forecasting
Mohammad Sina Jahangir, John You, John Quilty
Journal of Hydrology (2023) Vol. 619, pp. 129269-129269
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence
Sinan Rasiya Koya, Tirthankar Roy
Journal of Hydrology (2024) Vol. 637, pp. 131301-131301
Open Access | Times Cited: 19
Urban real-time rainfall-runoff prediction using adaptive SSA-decomposition with dual attention
Yuan Tian, Weiming Fu, Yi Xiang, et al.
Journal of Hydrology (2025), pp. 132701-132701
Closed Access | Times Cited: 1
A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China
Jinjie Fang, Linshan Yang, Xiaohu Wen, et al.
Hydrology Research (2024) Vol. 55, Iss. 2, pp. 180-204
Open Access | Times Cited: 9
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources
Zhong-kai Feng, J. Zhang, Wen-jing Niu
Applied Soft Computing (2024), pp. 112352-112352
Closed Access | Times Cited: 8
Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model
Hejiang Cai, Haiyun Shi, Zhaoqiang Zhou, et al.
Water Resources Research (2024) Vol. 60, Iss. 7
Open Access | Times Cited: 7
A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting
Chengjing Xu, Ping‐an Zhong, Feilin Zhu, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131494-131494
Closed Access | Times Cited: 5
Analysis of changes in the ecohydrological situation and its driving forces in the Li River Basin
Hongxiang Wang, Yanhua Li, Siyuan Cheng, et al.
Journal of Water and Climate Change (2025)
Open Access
VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting
Jiadong Liu, Teng Xu, Chunhui Lu
Journal of Hydrology (2025), pp. 132769-132769
Closed Access
Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi, Nicolas Masseï, Abderrahim Jardani, et al.
Hydrology and earth system sciences (2025) Vol. 29, Iss. 4, pp. 841-861
Open Access
Deep learning model for flood probabilistic forecasting considering spatiotemporal rainfall distribution and hydrologic uncertainty
Xin Xiang, Shenglian Guo, C. Li, et al.
Journal of Hydrology (2025), pp. 132879-132879
Closed Access
Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement
Xungui Li, Jian Sun, Qiyong Yang, et al.
Water Resources Research (2025) Vol. 61, Iss. 3
Open Access
Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network
Md Abdullah Al Mehedi, Achira Amur, Jessica L. Metcalf, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130076-130076
Open Access | Times Cited: 13
A process-driven deep learning hydrological model for daily rainfall-runoff simulation
Heng Li, Chunxiao Zhang, W. P. Chu, et al.
Journal of Hydrology (2024) Vol. 637, pp. 131434-131434
Closed Access | Times Cited: 4
Enhancing Hydro-climatic and land parameter forecasting using Transformer networks
Suchismita Subhadarsini, D. Nagesh Kumar, Rao S. Govindaraju
Journal of Hydrology (2025), pp. 132906-132906
Closed Access
Multi-step regional rainfall-runoff modeling using pyramidal transformer
Hanlin Yin, Zhao Xu, Xiuwei Zhang, et al.
Journal of Hydrology (2025), pp. 132935-132935
Closed Access
Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder
Mohammad Sina Jahangir, John Quilty
Journal of Hydrology (2023) Vol. 629, pp. 130498-130498
Closed Access | Times Cited: 9
Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks
Sivarama Krishna Reddy Chidepudi, Nicolas Masseï, Abderrahim Jardani, et al.
Journal of Hydrology Regional Studies (2023) Vol. 51, pp. 101632-101632
Open Access | Times Cited: 8
Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning
Songsong Wang, Ouguan Xu
IEEE Access (2024) Vol. 12, pp. 47830-47841
Open Access | Times Cited: 2
Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data
Songsong Wang, Ouguan Xu
IEEE Access (2024) Vol. 12, pp. 82706-82719
Open Access | Times Cited: 2
Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors
Jinghan Huang, Zhaocai Wang, Jinghan Dong, et al.
Stochastic Environmental Research and Risk Assessment (2024)
Closed Access | Times Cited: 2
Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting
Osama A. Abozweita, Ali Najah Ahmed, Lariyah Mohd Sidek, et al.
Journal of Hydroinformatics (2024)
Open Access | Times Cited: 2
Bayesian extreme learning machines for hydrological prediction uncertainty
John Quilty, Mohammad Sina Jahangir, John You, et al.
Journal of Hydrology (2023) Vol. 626, pp. 130138-130138
Closed Access | Times Cited: 6
Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi, Nicolas Masseï, Abderrahim Jardani, et al.
(2024)
Open Access | Times Cited: 1
Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting
Jiadong Liu, Teng Xu, Chunhui Lu, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Development of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting
Arvin Samadi-Koucheksaraee, Xuefeng Chu
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
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