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:

Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting
Jinwook Lee, Sayed M. Bateni, Changhyun Jun, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108744-108744
Closed Access | Times Cited: 8

Showing 8 citing articles:

A hybrid time series and physics-informed machine learning framework to predict soil water content
Amirsalar Bagheri, Andres Patrignani, Behzad Ghanbarian, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110105-110105
Closed Access | Times Cited: 1

Improving Reference Evapotranspiration Predictions with Hybrid Modeling Approach
Rimsha Habeeb, Mohammed M. A. Almazah, Ijaz Hussain, et al.
Earth Systems and Environment (2025)
Closed Access

An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management
Jian Dong, Yuan Xu, Rigeng Wu, et al.
Engineering Science and Technology an International Journal (2025) Vol. 63, pp. 101994-101994
Closed Access

Assessing salinity-induced impacts on plant transpiration through machine learning: from model development to deployment
Niguss Solomon Hailegnaw, Girma Worku Awoke, Aline de Camargo Santos, et al.
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 3
Closed Access

Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning
Arman Ahmadi, André Daccache, Minxue He, et al.
Journal of Hydrology Regional Studies (2025) Vol. 59, pp. 102339-102339
Closed Access

Dynamic optimization can effectively improve the accuracy of reference evapotranspiration in southern China
Xiang Xiao, Ziniu Xiao, Xiaogang Liu, et al.
Computers and Electronics in Agriculture (2024) Vol. 230, pp. 109881-109881
Closed Access | Times Cited: 1

Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques
Jalil Helali, Mehdi Mohammadi Ghaleni, Ameneh Mianabadi, et al.
Applied Water Science (2024) Vol. 14, Iss. 10
Open Access

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