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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:

Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions
Kuai Fang, Daniel Kifer, Kathryn Lawson, et al.
Water Resources Research (2020) Vol. 56, Iss. 12
Open Access | Times Cited: 87

Showing 1-25 of 87 citing articles:

Deep Learning for Geophysics: Current and Future Trends
Siwei Yu, Jianwei Ma
Reviews of Geophysics (2021) Vol. 59, Iss. 3
Open Access | Times Cited: 322
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Andreas Wünsch, Tanja Liesch, Stefan Broda
Hydrology and earth system sciences (2021) Vol. 25, Iss. 3, pp. 1671-1687
Open Access | Times Cited: 215
From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
Wei Zhi, Dapeng Feng, Wen‐Ping Tsai, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 4, pp. 2357-2368
Closed Access | Times Cited: 213
Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions
Kai Ma, Dapeng Feng, Kathryn Lawson, et al.
Water Resources Research (2021) Vol. 57, Iss. 5
Closed Access | Times Cited: 176
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
Thomas Lees, Marcus Buechel, Bailey Anderson, et al.
Hydrology and earth system sciences (2021) Vol. 25, Iss. 10, pp. 5517-5534
Open Access | Times Cited: 172
Differentiable modelling to unify machine learning and physical models for geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 552-567
Closed Access | Times Cited: 167
Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel
Kuai Fang, Chaopeng Shen
Journal of Hydrometeorology (2020) Vol. 21, Iss. 3, pp. 399-413
Open Access | Times Cited: 140
The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson, et al.
Water Resources Research (2022) Vol. 58, Iss. 4
Open Access | Times Cited: 134
A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data
Jiangtao Liu, Farshid Rahmani, Kathryn Lawson, et al.
Geophysical Research Letters (2022) Vol. 49, Iss. 7
Closed Access | Times Cited: 72
Alternate pathway for regional flood frequency analysis in data-sparse region
Nikunj K. Mangukiya, Ashutosh Sharma
Journal of Hydrology (2024) Vol. 629, pp. 130635-130635
Closed Access | Times Cited: 23
Editorial: Broadening the Use of Machine Learning in Hydrology
Chaopeng Shen, Xingyuan Chen, Eric Laloy
Frontiers in Water (2021) Vol. 3
Open Access | Times Cited: 103
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
Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea
Quang Viet Ly, Xuan Cuong Nguyen, Ngoc C. Lê, et al.
The Science of The Total Environment (2021) Vol. 797, pp. 149040-149040
Closed Access | Times Cited: 73
Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins
Farshid Rahmani, Chaopeng Shen, Samantha K. Oliver, et al.
Hydrological Processes (2021) Vol. 35, Iss. 11
Open Access | Times Cited: 65
Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models
Dayang Li, Lucy Marshall, Zhongmin Liang, et al.
Water Resources Research (2021) Vol. 57, Iss. 9
Closed Access | Times Cited: 62
Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars
Xuan Cuong Nguyen, Quang Viet Ly, Thi Thanh Huyen Nguyen, et al.
Chemosphere (2021) Vol. 287, pp. 132203-132203
Closed Access | Times Cited: 61
Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management
Samuel C. Zipper, William Farmer, Andrea E. Brookfield, et al.
JAWRA Journal of the American Water Resources Association (2022) Vol. 58, Iss. 2, pp. 289-312
Open Access | Times Cited: 42
Applying Knowledge-Guided Machine Learning to Slope Stability Prediction
Te Pei, Tong Qiu, Chaopeng Shen
Journal of Geotechnical and Geoenvironmental Engineering (2023) Vol. 149, Iss. 10
Closed Access | Times Cited: 28
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: 25
Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches
Hongliang Ma, Jiangyuan Zeng, Xiang Zhang, et al.
Remote Sensing of Environment (2024) Vol. 308, pp. 114197-114197
Closed Access | Times Cited: 13
A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs
Vinh Ngoc Tran, V. Y. Ivanov, Tien Giang Nguyen, et al.
Journal of Hydrology (2024) Vol. 629, pp. 130608-130608
Closed Access | Times Cited: 11
Prediction of chlorophyll-a as an indicator of harmful algal blooms using deep learning with Bayesian approximation for uncertainty assessment
Ibrahim Busari, Debabrata Sahoo, R.B. Jana
Journal of Hydrology (2024) Vol. 630, pp. 130627-130627
Closed Access | Times Cited: 11
Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments
Kai Ma, Daming He, Shiyin Liu, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130841-130841
Open Access | Times Cited: 11
Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network
Dayang Li, Lucy Marshall, Zhongmin Liang, et al.
Journal of Hydrology (2021) Vol. 603, pp. 126888-126888
Closed Access | Times Cited: 41
Field Data Forecasting Using LSTM and Bi-LSTM Approaches
Paweena Suebsombut, Aïcha Sekhari, Pradorn Sureephong, et al.
Applied Sciences (2021) Vol. 11, Iss. 24, pp. 11820-11820
Open Access | Times Cited: 41
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