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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:

Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience
Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert‐Uphoff
Artificial Intelligence for the Earth Systems (2022) Vol. 1, Iss. 4
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Data-driven predictions of the time remaining until critical global warming thresholds are reached
Noah S. Diffenbaugh, Elizabeth A. Barnes
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 6
Open Access | Times Cited: 111
Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset
Antonios Mamalakis, Imme Ebert‐Uphoff, Elizabeth A. Barnes
Environmental Data Science (2022) Vol. 1
Open Access | Times Cited: 76
Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions
Lü Liang, Jacob Daniels, Colleen P. Bailey, et al.
Environmental Pollution (2023) Vol. 331, pp. 121832-121832
Open Access | Times Cited: 46
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 26
Explainable artificial intelligence and interpretable machine learning for agricultural data analysis
Masahiro Ryo
Artificial Intelligence in Agriculture (2022) Vol. 6, pp. 257-265
Open Access | Times Cited: 61
Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives
Stefano Materia, Lluís Palma García, Chiem van Straaten, et al.
Wiley Interdisciplinary Reviews Climate Change (2024)
Open Access | Times Cited: 15
Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 14
Interpretable machine learning for weather and climate prediction: A review
Ruyi Yang, Jingyu Hu, Zihao Li, et al.
Atmospheric Environment (2024) Vol. 338, pp. 120797-120797
Closed Access | Times Cited: 13
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets
Miquel Miró-Nicolau, Antoni Jaume-i-Capó, Gabriel Moyà-Alcover
Artificial Intelligence (2024) Vol. 335, pp. 104179-104179
Open Access | Times Cited: 8
Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2
Emily M. Gordon, Elizabeth A. Barnes
Geophysical Research Letters (2022) Vol. 49, Iss. 15
Open Access | Times Cited: 32
Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics
Mariana Clare, Maike Sonnewald, Redouane Lguensat, et al.
Journal of Advances in Modeling Earth Systems (2022) Vol. 14, Iss. 11
Open Access | Times Cited: 32
A reliable feature-assisted contrastive generalization net for intelligent fault diagnosis under unseen machines and working conditions
Zhen Shi, Jinglong Chen, Xinwei Zhang, et al.
Mechanical Systems and Signal Processing (2022) Vol. 188, pp. 110011-110011
Closed Access | Times Cited: 32
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
Antonios Mamalakis, Imme Ebert‐Uphoff, Elizabeth A. Barnes
Lecture notes in computer science (2022), pp. 315-339
Open Access | Times Cited: 29
Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection‐Permitting Simulations for Data‐Driven Parameterizations
Y. Qiang Sun, Pedram Hassanzadeh, M. Joan Alexander, et al.
Journal of Advances in Modeling Earth Systems (2023) Vol. 15, Iss. 5
Open Access | Times Cited: 16
Exploring the decision-making process of ensemble learning algorithms in landslide susceptibility mapping: Insights from local and global explainable AI analyses
Alihan Teke, Taşkın Kavzoǧlu
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3765-3785
Closed Access | Times Cited: 6
Machine learning–based extreme event attribution
Jared T. Trok, Elizabeth A. Barnes, Frances V. Davenport, et al.
Science Advances (2024) Vol. 10, Iss. 34
Open Access | Times Cited: 6
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience
Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert‐Uphoff
Artificial Intelligence for the Earth Systems (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 22
Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect
Maria Rugenstein, Senne Van Loon, Elizabeth A. Barnes
Geophysical Research Letters (2025) Vol. 52, Iss. 4
Open Access
Using feature importance as an exploratory data analysis tool on Earth system models
Daniel Ries, Katherine Goode, Kellie McClernon, et al.
Geoscientific model development (2025) Vol. 18, Iss. 4, pp. 1041-1065
Open Access
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Tim Radke, Susanne Fuchs, Christian Wilms, et al.
Geoscientific model development (2025) Vol. 18, Iss. 4, pp. 1017-1039
Open Access
Sea Surface Salinity Provides Subseasonal Predictability for Forecasts of Opportunity of U.S. Summertime Precipitation
Marybeth C. Arcodia, Elizabeth A. Barnes, Paul J. Durack, et al.
Journal of Geophysical Research Atmospheres (2025) Vol. 130, Iss. 6
Open Access
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches
José González-Abad, Jorge Baño‐Medina, José Manuel Azcona
Journal of Advances in Modeling Earth Systems (2023) Vol. 15, Iss. 11
Open Access | Times Cited: 11
Characterizing climate pathways using feature importance on echo state networks
Katherine Goode, Daniel Ries, Kellie McClernon
Statistical Analysis and Data Mining The ASA Data Science Journal (2024) Vol. 17, Iss. 4
Open Access | Times Cited: 4
Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
Jared Willard, Charuleka Varadharajan, Xiaowei Jia, et al.
Environmental Data Science (2025) Vol. 4
Open Access
Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks
Michail Mamalakis, Antonios Mamalakis, Ingrid Agartz, et al.
AI Open (2025)
Open Access
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