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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:

Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling
Debaditya Chakraborty, Hakan Başağaoğlu, James Winterle
Expert Systems with Applications (2020) Vol. 170, pp. 114498-114498
Closed Access | Times Cited: 100

Showing 1-25 of 100 citing articles:

Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost
Ziqi Li
Computers Environment and Urban Systems (2022) Vol. 96, pp. 101845-101845
Open Access | Times Cited: 370
Interpretable and explainable AI (XAI) model for spatial drought prediction
Abhirup Dikshit, Biswajeet Pradhan
The Science of The Total Environment (2021) Vol. 801, pp. 149797-149797
Closed Access | Times Cited: 152
Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence
Debaditya Chakraborty, Arafat Alam, Saptarshi Chaudhuri, et al.
Applied Energy (2021) Vol. 291, pp. 116807-116807
Closed Access | Times Cited: 117
The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning
Sami Ben Jabeur, Rabeh Khalfaoui, Wissal Ben Arfi
Journal of Environmental Management (2021) Vol. 298, pp. 113511-113511
Open Access | Times Cited: 110
An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation
Ali El Bilali, Taleb Abdeslam, Ayoub Nafii, et al.
Journal of Environmental Management (2022) Vol. 327, pp. 116890-116890
Closed Access | Times Cited: 99
Research on Water Resource Modeling Based on Machine Learning Technologies
Liu Ze, Jingzhao Zhou, Xiaoyang Yang, et al.
Water (2024) Vol. 16, Iss. 3, pp. 472-472
Open Access | Times Cited: 21
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
Hakan Başağaoğlu, Debaditya Chakraborty, Cesar Do Lago, et al.
Water (2022) Vol. 14, Iss. 8, pp. 1230-1230
Open Access | Times Cited: 55
Model Predictive Control of water resources systems: A review and research agenda
Andrea Castelletti, Andrea Ficchì, Andrea Cominola, et al.
Annual Reviews in Control (2023) Vol. 55, pp. 442-465
Open Access | Times Cited: 39
Explainable artificial intelligence in information systems: A review of the status quo and future research directions
Julia Brasse, Hanna Rebecca Broder, Maximilian Förster, et al.
Electronic Markets (2023) Vol. 33, Iss. 1
Open Access | Times Cited: 36
Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models
Nicolae Stef, Hakan Başağaoğlu, Debaditya Chakraborty, et al.
Energy Economics (2023) Vol. 124, pp. 106822-106822
Closed Access | Times Cited: 34
Explainable artificial intelligence modeling to forecast bitcoin prices
John W. Goodell, Sami Ben Jabeur, Foued Saâdaoui, et al.
International Review of Financial Analysis (2023) Vol. 88, pp. 102702-102702
Closed Access | Times Cited: 30
Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price
Yefu Chen, Junfeng Jiao, Arya Farahi
Cities (2023) Vol. 140, pp. 104432-104432
Open Access | Times Cited: 23
Explainable deep learning for insights in El Niño and river flows
Yumin Liu, Kate Duffy, Jennifer Dy, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 22
Machine learning in energy storage material discovery and performance prediction
Guo-Chang Huang, Fuqiang Huang, Wujie Dong
Chemical Engineering Journal (2024) Vol. 492, pp. 152294-152294
Closed Access | Times Cited: 14
Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China
Shiqi Zhou, Weiyi Jia, Mo Wang, et al.
Journal of Environmental Management (2024) Vol. 369, pp. 122330-122330
Closed Access | Times Cited: 8
Accurate prediction of in-channel condensation heat transfer performance for natural gas liquefaction based on machine learning models and correlations
Kai Wang, Jinglei Wang, Shaolong Zhu, et al.
Applied Thermal Engineering (2025), pp. 125451-125451
Closed Access | Times Cited: 1
Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA
Futian Weng, Jianping Zhu, Cai Yang, et al.
Expert Systems with Applications (2022) Vol. 210, pp. 118482-118482
Closed Access | Times Cited: 29
Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings
Antonio Galli, Marco Savino Piscitelli, Vincenzo Moscato, et al.
Expert Systems with Applications (2022) Vol. 206, pp. 117649-117649
Closed Access | Times Cited: 28
Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing
Ziqi Li
Travel Behaviour and Society (2023) Vol. 31, pp. 284-294
Open Access | Times Cited: 21
Development of transparent high-frequency soft sensor of total nitrogen and total phosphorus concentrations in rivers using stacked convolutional auto-encoder and explainable AI
Abdulrahman H. Ba-Alawi, SungKu Heo, Hanaa Aamer, et al.
Journal of Water Process Engineering (2023) Vol. 53, pp. 103661-103661
Closed Access | Times Cited: 17
Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data
Xin Zhao, Lei Zhang, Ge Zhu, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108140-108140
Closed Access | Times Cited: 17
Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate
Ali Raza, Romana Fahmeed, Neyha Rubab Syed, et al.
Water (2023) Vol. 15, Iss. 21, pp. 3822-3822
Open Access | Times Cited: 17
Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series
Serkan Kartal, Muzaffer Can İban, Aliihsan Şekertekin
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 12, pp. 18932-18948
Open Access | Times Cited: 7
A Novel Fusion-Based Methodology for Drought Forecasting
Huihui Zhang, Hugo A. Loáiciga, Tobias Sauter
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 828-828
Open Access | Times Cited: 6
Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management
Debaditya Chakraborty, Hakan Başağaoğlu, Lilianna Gutierrez, et al.
Environmental Research Letters (2021) Vol. 16, Iss. 11, pp. 114024-114024
Open Access | Times Cited: 35
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