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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 note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, et al.
Hydrology and earth system sciences (2021) Vol. 25, Iss. 5, pp. 2685-2703
Open Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Deep learning rainfall–runoff predictions of extreme events
Jonathan Frame, Frederik Kratzert, Daniel Klotz, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 13, pp. 3377-3392
Open Access | Times Cited: 208
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
Martin Gauch, Frederik Kratzert, Daniel Klotz, et al.
Hydrology and earth system sciences (2021) Vol. 25, Iss. 4, pp. 2045-2062
Open Access | Times Cited: 182
Uncertainty estimation with deep learning for rainfall–runoff modeling
Daniel Klotz, Frederik Kratzert, Martin Gauch, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 6, pp. 1673-1693
Open Access | Times Cited: 136
Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change
Sungwook Wi, Scott Steinschneider
Water Resources Research (2022) Vol. 58, Iss. 9
Closed Access | Times Cited: 86
Hybrid forecasting: blending climate predictions with AI models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 9, pp. 1865-1889
Open Access | Times Cited: 84
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Dapeng Feng, Hylke E. Beck, Kathryn Lawson, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 12, pp. 2357-2373
Open Access | Times Cited: 55
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: 20
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 17, pp. 4187-4201
Open Access | Times Cited: 19
The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset
Georgy Ayzel, Maik Heistermann
Computers & Geosciences (2021) Vol. 149, pp. 104708-104708
Closed Access | Times Cited: 88
Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy
Wenyu Ouyang, Kathryn Lawson, Dapeng Feng, et al.
Journal of Hydrology (2021) Vol. 599, pp. 126455-126455
Closed Access | Times Cited: 77
Improving hydrologic models for predictions and process understanding using neural ODEs
Marvin Höge, Andreas Scheidegger, Marco Baity‐Jesi, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 19, pp. 5085-5102
Open Access | Times Cited: 48
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Alexander Y. Sun, Peishi Jiang, Zong‐Liang Yang, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 19, pp. 5163-5184
Open Access | Times Cited: 42
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
Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins
Senlin Tang, Fubao Sun, Wenbin Liu, et al.
Water Resources Research (2023) Vol. 59, Iss. 7
Closed Access | Times Cited: 32
On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process
Jonathan Frame, Frederik Kratzert, Hoshin V. Gupta, et al.
Hydrological Processes (2023) Vol. 37, Iss. 3
Closed Access | Times Cited: 29
Enhancing process-based hydrological models with embedded neural networks: A hybrid approach
Bu Li, Ting Sun, Fuqiang Tian, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130107-130107
Open Access | Times Cited: 27
Predicting streamflow with LSTM networks using global datasets
Katharina Wilbrand, Riccardo Taormina, Marie‐Claire ten Veldhuis, et al.
Frontiers in Water (2023) Vol. 5
Open Access | Times Cited: 24
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Sungwook Wi, Scott Steinschneider
Hydrology and earth system sciences (2024) Vol. 28, Iss. 3, pp. 479-503
Open Access | Times Cited: 15
Probing the limit of hydrologic predictability with the Transformer network
Jiangtao Liu, Yuchen Bian, Kathryn Lawson, et al.
Journal of Hydrology (2024) Vol. 637, pp. 131389-131389
Open Access | Times Cited: 12
Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes
Arken Tursun, Xianhong Xie, Yibing Wang, et al.
Journal of Hydrology (2024) Vol. 630, pp. 130771-130771
Closed Access | Times Cited: 10
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets
F. M. Hasan, Paul Medley, Jason Drake, et al.
Water (2024) Vol. 16, Iss. 13, pp. 1904-1904
Open Access | Times Cited: 8
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Pin Shuai, Xingyuan Chen, Utkarsh Mital, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 8, pp. 2245-2276
Open Access | Times Cited: 32
Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions
Sung-Hyun Yoon, Kuk‐Hyun Ahn
Journal of Hydrology (2024) Vol. 632, pp. 130862-130862
Closed Access | Times Cited: 7
Development of objective function-based ensemble model for streamflow forecasts
Yongen Lin, Dagang Wang, Jinxin Zhu, et al.
Journal of Hydrology (2024) Vol. 632, pp. 130861-130861
Closed Access | Times Cited: 6
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
Daniel Klotz, Frederik Kratzert, Martin Gauch, et al.
EarthArXiv (California Digital Library) (2020)
Open Access | Times Cited: 49
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