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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:

Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination
Hamid Reza Pourghasemi, Soheila Pouyan, Mojgan Bordbar, et al.
Natural Hazards (2023) Vol. 116, Iss. 3, pp. 3797-3816
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 20
Quantitative assessment of the GLOF risk along China-Nepal transboundary basins by integrating remote sensing, machine learning, and hydrodynamic model
Manish Raj Gouli, Kaiheng Hu, Nitesh Khadka, et al.
International Journal of Disaster Risk Reduction (2025), pp. 105231-105231
Closed Access | Times Cited: 4
FFM: Flood Forecasting Model Using Federated Learning
Muhammad Shoaib Farooq, Rabia Tehseen, Junaid Nasir Qureshi, et al.
IEEE Access (2023) Vol. 11, pp. 24472-24483
Open Access | Times Cited: 28
Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere
Jing Luo, Guoan Yin, Fujun Niu, et al.
Advances in Climate Change Research (2024) Vol. 15, Iss. 2, pp. 253-264
Open Access | Times Cited: 6
Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine
Tianwu Ma, Gang Wang, Rui Guo, et al.
Journal of Environmental Management (2024) Vol. 359, pp. 120966-120966
Closed Access | Times Cited: 6
Accurate vegetation destruction detection using remote sensing imagery based on the three-band difference vegetation index (TBDVI) and dual-temporal detection method
Chuanwu Zhao, Yaozhong Pan, Shoujia Ren, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 127, pp. 103669-103669
Open Access | Times Cited: 5
Unveiling the thermal impact of land cover transformations in Khuzestan province through MODIS satellite remote sensing products
Iraj Baronian, Reza Borna, Kamran Jafarpour Ghalehteimouri, et al.
Paddy and Water Environment (2024) Vol. 22, Iss. 4, pp. 503-520
Closed Access | Times Cited: 5
Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning
Ishita Afreen Ahmed, Swapan Talukdar, Mirza Razi Imam Baig, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 33, pp. 101088-101088
Closed Access | Times Cited: 13
Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation
Huu Duy Nguyen, Dinh Kha Dang, Nhu Y Nguyen, et al.
Journal of Water and Climate Change (2023) Vol. 15, Iss. 1, pp. 284-304
Open Access | Times Cited: 13
Fire Risk Mapping Using Machine Learning Method and Remote Sensing in the Mediterranean Region
Fatih Sivrikaya, Döndü Demirel
Advances in Space Research (2025)
Closed Access
Urban construction promotes geological disasters: evidence from prediction of future urban road collapse scenario in Hangzhou, China
Bofan Yu, Jiaxing Yan, Yuan Li, et al.
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2025), pp. 1-21
Closed Access
A comprehensive bibliometric review of forest fires in Iran
Saeedreza Moazeni, Artemi Cerdà
Environmental Development (2025), pp. 101160-101160
Closed Access
Machine learning and GIS-based multi-hazard risk modeling for Uttarakhand: Integrating seismic, landslide, and flood susceptibility with socioeconomic vulnerability
Vipin Chauhan, Laxmi Gupta, Jagabandhu Dixit
Environmental and Sustainability Indicators (2025), pp. 100664-100664
Open Access
EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN
Gulnur Kazbekova, Arypzhan Aben, Anuarbek Amanov, et al.
Scientific Journal of Astana IT University (2025)
Open Access
Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province
Weiting Yue, Yunji Gao, Yao Xiao, et al.
Research Square (Research Square) (2025)
Closed Access
How do hydrogeological and socio-economic parameters influence the likelihood of NO3− pollution and Cl− salinization? An application within the campania region (Italy)
Mojgan Bordbar, Gianluigi Busico, Stefania Stevenazzi, et al.
Natural Hazards (2025)
Open Access
Robustness study of seismic hazard assessment models based on multi-source data
Bo Liu, Haijia Wen, Mingrui Di, et al.
Gondwana Research (2025) Vol. 144, pp. 87-108
Closed Access
Effects of different division methods of landslide susceptibility levels on regional landslide susceptibility mapping
Faming Huang, Yang Yong, Bingchen Jiang, et al.
Bulletin of Engineering Geology and the Environment (2025) Vol. 84, Iss. 6
Closed Access
Mapping wildfire susceptibility in the tropical region of Brunei: a machine learning and explainable AI approach using google earth engine with remote sensing data
Rufai Yusuf Zakari, Owais Ahmed Malik, Ong Wee-Hong
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access
Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam
Huu Duy Nguyen, Dinh Kha Dang, Quang‐Thanh Bui, et al.
Transactions in GIS (2023) Vol. 27, Iss. 5, pp. 1614-1640
Closed Access | Times Cited: 9
An assessment of existing wildfire danger indices in comparison to one-class machine learning models
Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish, et al.
Natural Hazards (2024)
Open Access | Times Cited: 3
Development of a new indicator for identifying vegetation destruction events using remote sensing data
Chuanwu Zhao, Yaozhong Pan, Peng Zhang
Ecological Indicators (2024) Vol. 166, pp. 112553-112553
Open Access | Times Cited: 3
Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran
Mohammad Bashirgonbad, Behnoush Farokhzadeh, Vahid Gholami
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 38, pp. 50427-50442
Closed Access | Times Cited: 2
Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India
Barnali Kundu, Narendra Kumar Rana, Sonali Kundu, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 54, pp. 63098-63119
Closed Access | Times Cited: 2
Integrating multi-hazard susceptibility and building exposure: A case study for Quang Nam province, Vietnam
Chinh Luu, Giuseppe Forino, Lynda Yorke, et al.
(2024)
Open Access | Times Cited: 1
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