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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:

Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack
Emrehan Kutluğ Şahin, İsmail Çölkesen, Suheda Semih Acmali, et al.
Computers & Geosciences (2020) Vol. 144, pp. 104592-104592
Closed Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy
Taorui Zeng, Liyang Wu, Dario Peduto, et al.
Geoscience Frontiers (2023) Vol. 14, Iss. 6, pp. 101645-101645
Open Access | Times Cited: 76
Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China
Hanxiang Xiong, Chuanming Ma, Minghong Li, et al.
The Science of The Total Environment (2023) Vol. 866, pp. 161430-161430
Closed Access | Times Cited: 54
Flood susceptible prediction through the use of geospatial variables and machine learning methods
Navid Mahdizadeh Gharakhanlou, Liliana Pérez
Journal of Hydrology (2023) Vol. 617, pp. 129121-129121
Closed Access | Times Cited: 54
PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment
Zizheng Guo, Haojie Wang, Jun He, et al.
Environmental Modelling & Software (2025) Vol. 186, pp. 106367-106367
Closed Access | Times Cited: 5
Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping
Emrehan Kutluğ Şahin
Geocarto International (2020) Vol. 37, Iss. 9, pp. 2441-2465
Closed Access | Times Cited: 120
Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data
Selçuk Demir, Emrehan Kutluğ Şahin
Soil Dynamics and Earthquake Engineering (2021) Vol. 154, pp. 107130-107130
Closed Access | Times Cited: 59
Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method
Faming Huang, Zuokui Teng, Chi Yao, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 16, Iss. 1, pp. 213-230
Open Access | Times Cited: 31
Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River
Jiaming Yao, Xin Yao, Zheng Zhao, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 26
Comparison of LiDAR- and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm
Felicia França Pereira, Tatiana Sussel Gonçalves Mendes, Silvio Jorge Coelho Simões, et al.
Landslides (2023) Vol. 20, Iss. 3, pp. 579-600
Closed Access | Times Cited: 25
Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models
Cheng Chen, Lei Fan
Stochastic Environmental Research and Risk Assessment (2023)
Open Access | Times Cited: 23
Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy
Mohamed M. Abdelkader, Árpád Csámer
Natural Hazards (2025)
Open Access | Times Cited: 1
Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Mahdis Amiri, et al.
Natural Hazards (2021) Vol. 108, Iss. 1, pp. 1291-1316
Closed Access | Times Cited: 45
Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan
Ahmad Hammad Khaliq, Muhammad Basharat, Malik Talha Riaz, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 3, pp. 101907-101907
Open Access | Times Cited: 36
Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region
Sunil Saha, Anik Saha, Tusar Kanti Hembram, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 10, pp. 3597-3616
Closed Access | Times Cited: 32
An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox
Wubiao Huang, Mingtao Ding, Zhenhong Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3408-3408
Open Access | Times Cited: 31
Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost
Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2022) Vol. 37, Iss. 3, pp. 1067-1092
Closed Access | Times Cited: 30
Landslide susceptibility assessment using locally weighted learning integrated with machine learning algorithms
Haoyuan Hong
Expert Systems with Applications (2023) Vol. 237, pp. 121678-121678
Closed Access | Times Cited: 18
Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms
Abdul Baser Qasimi, Vahid Isazade, Enayatullah Enayat, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 17
Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review
Santos D. Chicas, Heng Li, Nobuya Mizoue, et al.
Natural Hazards (2024) Vol. 120, Iss. 14, pp. 12573-12593
Closed Access | Times Cited: 8
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
Omar F. Althuwaynee, Sang-Wan Kim, Mohamed A. Najemaden, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 32, pp. 43544-43566
Closed Access | Times Cited: 39
Shaping Sustainable Urban Environments by Addressing the Hydro-Meteorological Factors in Landslide Occurrence: Ciuperca Hill (Oradea, Romania)
Cezar Morar, Tin Lukić, Biljana Basarin, et al.
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 9, pp. 5022-5022
Open Access | Times Cited: 37
A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4521-4521
Open Access | Times Cited: 36
Assessing geological hazard susceptibility and impacts of climate factors in the eastern Himalayan syntaxis region
Jiaming Yao, Yanbing Wang, Teng Wang, et al.
Landslides (2024) Vol. 21, Iss. 9, pp. 2195-2209
Closed Access | Times Cited: 6
PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis
Mohammed Basheer, Thomas Oommen
Environmental Modelling & Software (2024) Vol. 177, pp. 106055-106055
Open Access | Times Cited: 5
Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region
Mohib Ullah, Bingzhe Tang, Wenchao Huangfu, et al.
Land (2024) Vol. 13, Iss. 7, pp. 1011-1011
Open Access | Times Cited: 5
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