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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:

Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory
Aliakbar Mohammadifar, Hamid Gholami, Jesús Rodrigo‐Comino, et al.
CATENA (2021) Vol. 200, pp. 105178-105178
Closed Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process
Ahmad Hosseinzadeh, John L. Zhou, Ali Altaee, et al.
Bioresource Technology (2021) Vol. 343, pp. 126111-126111
Open Access | Times Cited: 114
An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost
Xinzhi Zhou, Haijia Wen, Ziwei Li, et al.
Geocarto International (2022) Vol. 37, Iss. 26, pp. 13419-13450
Closed Access | Times Cited: 114
Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations
Halit Enes Aydin, Muzaffer Can İban
Natural Hazards (2022) Vol. 116, Iss. 3, pp. 2957-2991
Closed Access | Times Cited: 89
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion
Hamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
The Science of The Total Environment (2023) Vol. 904, pp. 166960-166960
Closed Access | Times Cited: 48
Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid
Amir Hossein Navidpour, Ahmad Hosseinzadeh, Zhenguo Huang, et al.
Catalysis Reviews (2022) Vol. 66, Iss. 2, pp. 687-712
Closed Access | Times Cited: 41
Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy
Mohammad Mahbub Kabir, Sujit Kumar Roy, Faisal Alam, et al.
Desalination (2023) Vol. 567, pp. 116992-116992
Closed Access | Times Cited: 41
Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
Muzaffer Can İban, Süleyman Sefa Bilgilioğlu
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2243-2270
Closed Access | Times Cited: 34
Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models
Mahrooz Rezaei, Aliakbar Mohammadifar, Hamid Gholami, et al.
CATENA (2023) Vol. 223, pp. 106953-106953
Open Access | Times Cited: 32
Shapley values reveal the drivers of soil organic carbon stock prediction
Alexandre M.J.‐C. Wadoux, Nicolas Saby, Manuel Martín
SOIL (2023) Vol. 9, Iss. 1, pp. 21-38
Open Access | Times Cited: 29
Soil erodibility for water and wind erosion and its relationship to vegetation and soil properties in China's drylands
Yi Han, Wenwu Zhao, Jingyi Ding, et al.
The Science of The Total Environment (2023) Vol. 903, pp. 166639-166639
Closed Access | Times Cited: 27
Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms
Junfang Wang, Yuan Wang, Duanyang Xu
Ecological Informatics (2024) Vol. 82, pp. 102769-102769
Open Access | Times Cited: 10
Research on the Driving Effect of Marine Economy on the High-Quality Development of Regional Economy – Evidence from China’s Coastal Areas
Jianyue Ji, Yuhang Chi, Xingmin Yin
Regional Studies in Marine Science (2024) Vol. 74, pp. 103550-103550
Closed Access | Times Cited: 9
A SHAP-Enhanced XGBoost Model for Interpretable Prediction of Coseismic Landslides
Haijia Wen, Bo Liu, Mingrui Di, et al.
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3826-3854
Closed Access | Times Cited: 9
Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions
Marzieh Mokarram, Abdol Rassoul Zarei
Geoderma (2021) Vol. 403, pp. 115379-115379
Closed Access | Times Cited: 52
Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system
Hamid Gholami, Aliakbar Mohammadifar, Hossein Malakooti, et al.
Atmospheric Pollution Research (2021) Vol. 12, Iss. 9, pp. 101173-101173
Closed Access | Times Cited: 43
Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks
Hamid Gholami, Aliakbar Mohammadifar, Kathryn E. Fitzsimmons, et al.
Frontiers in Environmental Science (2023) Vol. 11
Open Access | Times Cited: 17
An explainable integrated machine learning model for mapping soil erosion by wind and water in a catchment with three desiccated lakes
Hamid Gholami, Mehdi Jalali, Marzieh Rezaei, et al.
Aeolian Research (2024) Vol. 67-69, pp. 100924-100924
Closed Access | Times Cited: 7
An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction
Mohamed Chaibi, El Mahjoub Benghoulam, Lhoussaine Tarik, et al.
Energies (2021) Vol. 14, Iss. 21, pp. 7367-7367
Open Access | Times Cited: 41
Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia
Mahdi Boroughani, Sima Pourhashemi, Hamid Gholami, et al.
Journal of Arid Land (2021) Vol. 13, Iss. 11, pp. 1103-1121
Open Access | Times Cited: 35
Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source
Hamid Gholami, Aliakbar Mohammadifar
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 28
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind
Hamid Gholami, Aliakbar Mohammadifar, Reza Dahmardeh Behrooz, et al.
Environmental Pollution (2023) Vol. 342, pp. 123082-123082
Closed Access | Times Cited: 15
An interpretable deep learning model to map land subsidence hazard
Paria Rahmani, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 11, pp. 17448-17460
Closed Access | Times Cited: 6
Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence
Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 10, pp. 26580-26595
Closed Access | Times Cited: 21
Assessing the influencing factors of soil susceptibility to wind erosion: A wind tunnel experiment with a machine learning and model-agnostic interpretation approach
Yang Zhao, Guanglei Gao, Guodong Ding, et al.
CATENA (2022) Vol. 215, pp. 106324-106324
Closed Access | Times Cited: 20
An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia
Saeed Alqadhi, Javed Mallick, Swapan Talukdar, et al.
Frontiers in Ecology and Evolution (2023) Vol. 11
Open Access | Times Cited: 13
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