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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:

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

Showing 1-25 of 57 citing articles:

Multi-scenario simulation of urban growth boundaries with an ESP-FLUS model: A case study of the Min Delta region, China
Xiaoyang Liu, Ming Wei, Zhigang Li, et al.
Ecological Indicators (2022) Vol. 135, pp. 108538-108538
Open Access | Times Cited: 89
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
The spatio-temporal dynamics of urban growth and population in metropolitan regions of Iran
Bagher Bagheri, Alì Soltani
Habitat International (2023) Vol. 136, pp. 102797-102797
Closed Access | Times Cited: 31
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: 29
How can SHAP (SHapley Additive exPlanations) interpretations improve deep learning based urban cellular automata model?
Changlan Yang, Xuefeng Guan, Qingyang Xu, et al.
Computers Environment and Urban Systems (2024) Vol. 111, pp. 102133-102133
Closed Access | Times Cited: 17
On the benefit of feature selection and ensemble feature selection for fuzzy k-nearest neighbor classification
Christoph Lohrmann, Alena Lohrmann, Mahinda Mailagaha Kumbure
Applied Soft Computing (2025), pp. 112784-112784
Closed Access | Times Cited: 1
Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China
Zhiwei Deng, Bin Quan
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 14, pp. 8491-8491
Open Access | Times Cited: 35
Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data
Muhammad Nasar Ahmad, Zhenfeng Shao, Akib Javed
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 12, pp. 32985-33001
Closed Access | Times Cited: 33
A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye
Öznur Işınkaralar, Çiğdem Varol
Cities (2022) Vol. 132, pp. 104073-104073
Closed Access | Times Cited: 31
Patterns and controls of ecosystem service values under different land-use change scenarios in a mining-dominated basin of northern China
Yingqing Su, Xiaohong Ma, Qi Feng, et al.
Ecological Indicators (2023) Vol. 151, pp. 110321-110321
Open Access | Times Cited: 23
The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan
Muhammad Nasar Ahmad, Zhenfeng Shao, Akib Javed, et al.
Photogrammetric Engineering & Remote Sensing (2023) Vol. 89, Iss. 1, pp. 47-55
Closed Access | Times Cited: 21
A novel explainable PSO-XGBoost model for regional flood frequency analysis at a national scale: Exploring spatial heterogeneity in flood drivers
Yousef Kanani‐Sadat, Abdolreza Safari, Mohsen Nasseri, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131493-131493
Closed Access | Times Cited: 8
A three-dimensional future land use simulation (FLUS-3D) model for simulating the 3D urban dynamics under the shared socio-economic pathways
Xiaocong Xu, Dan Ding, Xiaoping Liu
Landscape and Urban Planning (2024) Vol. 250, pp. 105135-105135
Closed Access | Times Cited: 8
Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods
Bin Zhang, Haijun Wang
GIScience & Remote Sensing (2021) Vol. 59, Iss. 1, pp. 71-95
Open Access | Times Cited: 40
Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand
Jianxin Yang, Wenwu Tang, Jian Gong, et al.
Landscape and Urban Planning (2022) Vol. 231, pp. 104640-104640
Closed Access | Times Cited: 28
Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin
Nirmal Kumar, Vikram Gaurav Singh, Sudhir Kumar Singh, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 49, pp. 107219-107235
Closed Access | Times Cited: 15
Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
Emre Tepe, Abolfazl Safikhani
Sustainable Cities and Society (2023) Vol. 90, pp. 104390-104390
Open Access | Times Cited: 14
What's going on? Urban agglomerations and firm green innovation: Evidence from Chengdu-Chongqing Economic Circle, China
Ruifeng Hu, Weiqiao Xu
Journal of Cleaner Production (2023) Vol. 414, pp. 137662-137662
Closed Access | Times Cited: 14
An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways
Yimin Chen
Computers Environment and Urban Systems (2021) Vol. 91, pp. 101727-101727
Open Access | Times Cited: 32
Simulation and impact assessment of future land use and land cover changes in two protected areas in Tehran, Iran
Parvaneh Sobhani, Hassan Esmaeilzadeh, Hossein Mostafavi
Sustainable Cities and Society (2021) Vol. 75, pp. 103296-103296
Closed Access | Times Cited: 28
Spatiotemporal patterns, driving mechanism, and multi-scenario simulation of urban expansion in Min Delta Region, China
Xiaoyang Liu, Yinfeng Li, Sen Zhang, et al.
Ecological Indicators (2023) Vol. 158, pp. 111312-111312
Open Access | Times Cited: 13
Spatial constraints in cellular automata-based urban growth models: A systematic comparison of classifiers and input urban maps
Cassiano Bastos Moroz, Tobias Sieg, Annegret H. Thieken
Computers Environment and Urban Systems (2024) Vol. 110, pp. 102118-102118
Closed Access | Times Cited: 4
Three novel cost-sensitive machine learning models for urban growth modelling
Mohammad Ahmadlou, Mohammad Karimi, Saad Sh. Sammen, et al.
Geocarto International (2024) Vol. 39, Iss. 1
Open Access | Times Cited: 4
Machine learning application to spatio-temporal modeling of urban growth
Yuna Kim, Abolfazl Safikhani, Emre Tepe
Computers Environment and Urban Systems (2022) Vol. 94, pp. 101801-101801
Closed Access | Times Cited: 18
Satellite-based prediction of surface dust mass concentration in southeastern Iran using an intelligent approach
Seyed Babak Haji Seyed Asadollah, Ahmad Sharafati, Davide Motta, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 10, pp. 3731-3745
Closed Access | Times Cited: 11
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