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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:

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales
Dimitrios Gounaridis, Ioannis Chorianopoulos, Elías Symeonakis, et al.
The Science of The Total Environment (2018) Vol. 646, pp. 320-335
Open Access | Times Cited: 164

Showing 1-25 of 164 citing articles:

Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China
Xun Liang, Qingfeng Guan, Keith Clarke, et al.
Computers Environment and Urban Systems (2020) Vol. 85, pp. 101569-101569
Open Access | Times Cited: 945
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Junye Wang, Michael Bretz, M. Ali Akber Dewan, et al.
The Science of The Total Environment (2022) Vol. 822, pp. 153559-153559
Closed Access | Times Cited: 227
Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study
Weibin Lin, Yimin Sun, Steffen Nijhuis, et al.
The Science of The Total Environment (2020) Vol. 739, pp. 139899-139899
Closed Access | Times Cited: 209
Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
Hamid Reza Pourghasemi, Narges Kariminejad, Mahdis Amiri, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 194
Evaluating the potential impacts of land use changes on ecosystem service value under multiple scenarios in support of SDG reporting: A case study of the Wuhan urban agglomeration
Kaifeng Peng, Weiguo Jiang, Ziyan Ling, et al.
Journal of Cleaner Production (2021) Vol. 307, pp. 127321-127321
Closed Access | Times Cited: 159
Beyond land cover change: towards a new generation of land use models
Peter H. Verburg, Peter Alexander, Tom Evans, et al.
Current Opinion in Environmental Sustainability (2019) Vol. 38, pp. 77-85
Open Access | Times Cited: 151
Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000–2019
Han Zhai, Chaoqun Lv, Wanzeng Liu, et al.
Remote Sensing (2021) Vol. 13, Iss. 16, pp. 3331-3331
Open Access | Times Cited: 141
The ecosystem service values simulation and driving force analysis based on land use/land cover: A case study in inland rivers in arid areas of the Aksu River Basin, China
Ling Xie, Hongwei Wang, Suhong Liu
Ecological Indicators (2022) Vol. 138, pp. 108828-108828
Open Access | Times Cited: 95
Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data
Zeeshan Zafar, Muhammad Zubair, Yuanyuan Zha, et al.
The Egyptian Journal of Remote Sensing and Space Science (2024) Vol. 27, Iss. 2, pp. 216-226
Open Access | Times Cited: 27
A Novel Method for Predicting Urban Residential Quality Distribution Based on Multi-Interest Consideration
Jiawen Ren, Xin Zhou, Jingjing An, et al.
Buildings (2025) Vol. 15, Iss. 2, pp. 192-192
Open Access | Times Cited: 1
Mixed-cell cellular automata: A new approach for simulating the spatio-temporal dynamics of mixed land use structures
Xun Liang, Qingfeng Guan, Keith Clarke, et al.
Landscape and Urban Planning (2020) Vol. 205, pp. 103960-103960
Closed Access | Times Cited: 108
Simulating wetland changes under different scenarios based on integrating the random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration
Kaifeng Peng, Weiguo Jiang, Yue Deng, et al.
Ecological Indicators (2020) Vol. 117, pp. 106671-106671
Open Access | Times Cited: 103
A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation
Weiran Xing, Yuehui Qian, Xuefeng Guan, et al.
Computers & Geosciences (2020) Vol. 137, pp. 104430-104430
Closed Access | Times Cited: 98
Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation
Yuehui Qian, Weiran Xing, Xuefeng Guan, et al.
The Science of The Total Environment (2020) Vol. 722, pp. 137738-137738
Closed Access | Times Cited: 81
Modeling ESV losses caused by urban expansion using cellular automata and geographically weighted regression
Shurui Chen, Yongjiu Feng, Xiaohua Tong, et al.
The Science of The Total Environment (2020) Vol. 712, pp. 136509-136509
Closed Access | Times Cited: 72
A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran
Soheila Pouyan, Hamid Reza Pourghasemi, Mojgan Bordbar, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 60
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran
Hossein Shafizadeh‐Moghadam, Masoud Minaei, Robert Gilmore Pontius, et al.
Computers Environment and Urban Systems (2021) Vol. 87, pp. 101595-101595
Closed Access | Times Cited: 57
Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation
Jie Liu, Lang Zhang, Qingping Zhang, et al.
Sustainable Cities and Society (2021) Vol. 66, pp. 102698-102698
Closed Access | Times Cited: 56
Simulating mixed land-use change under multi-label concept by integrating a convolutional neural network and cellular automata: a case study of Huizhou, China
Xinxin Wu, Xiaoping Liu, Dachuan Zhang, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 609-632
Open Access | Times Cited: 51
Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities
Pavlos Tsagkis, Efthimios Bakogiannis, Alexandros Nikitas
Sustainable Cities and Society (2022) Vol. 89, pp. 104337-104337
Open Access | Times Cited: 40
Simulating land use change for sustainable land management in rapid urbanization regions: a case study of the Yangtze River Delta region
Zhonghao Zhang, Xueting Wang, Yue Zhang, et al.
Landscape Ecology (2023) Vol. 38, Iss. 7, pp. 1807-1830
Closed Access | Times Cited: 34
Evaluation of future wetland changes under optimal scenarios and land degradation neutrality analysis in the Guangdong-Hong Kong-Macao Greater Bay Area
Kaifeng Peng, Weiguo Jiang, Xuejun Wang, et al.
The Science of The Total Environment (2023) Vol. 879, pp. 163111-163111
Closed Access | Times Cited: 28
A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation
Ye Zhou, Chen Huang, Tao Wu, et al.
Ecological Modelling (2023) Vol. 482, pp. 110394-110394
Closed Access | Times Cited: 27
Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed
Khandaker Iftekharul Islam, Emile Elias, Kenneth C. Carroll, et al.
Remote Sensing (2023) Vol. 15, Iss. 16, pp. 3999-3999
Open Access | Times Cited: 22
Prediction of land use for the next 30 years using the PLUS model's multi-scenario simulation in Guizhou Province, China
Juncong Liu, Bangyu Liu, Linjing Wu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 12
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