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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:

Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping
Bakhtiar Feizizadeh, Majid Shadman Roodposhti, Thomas Blaschke, et al.
Arabian Journal of Geosciences (2017) Vol. 10, Iss. 5
Closed Access | Times Cited: 110

Showing 1-25 of 110 citing articles:

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges
Rajesh Gupta, Sudeep Tanwar, Fadi Al‐Turjman, et al.
IEEE Access (2020) Vol. 8, pp. 24746-24772
Open Access | Times Cited: 214
Development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture
Javad Seyedmohammadi, Fereydoon Sarmadian, Ali Asghar Jafarzadeh, et al.
Geoderma (2019) Vol. 352, pp. 80-95
Closed Access | Times Cited: 173
Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania
Romulus Costache, Dieu Tien Bui
The Science of The Total Environment (2019) Vol. 691, pp. 1098-1118
Closed Access | Times Cited: 149
A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model
Fei Yan, Qiuwen Zhang, Ye Song, et al.
Geomorphology (2018) Vol. 327, pp. 170-187
Closed Access | Times Cited: 155
Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks
Husam A. H. Al-Najjar, Biswajeet Pradhan
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 625-637
Open Access | Times Cited: 139
Bagging based Support Vector Machines for spatial prediction of landslides
Binh Thai Pham, Dieu Tien Bui, Indra Prakash
Environmental Earth Sciences (2018) Vol. 77, Iss. 4
Closed Access | Times Cited: 132
Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine
Faming Huang, Chi Yao, Weiping Liu, et al.
Geomatics Natural Hazards and Risk (2018) Vol. 9, Iss. 1, pp. 919-938
Open Access | Times Cited: 112
A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping
Omid Ghorbanzadeh, Hashem Rostamzadeh, Thomas Blaschke, et al.
Natural Hazards (2018) Vol. 94, Iss. 2, pp. 497-517
Open Access | Times Cited: 110
A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling
Binh Thai Pham, Indra Prakash, Khabat Khosravi, et al.
Geocarto International (2018) Vol. 34, Iss. 13, pp. 1385-1407
Closed Access | Times Cited: 107
Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors
Xiangang Luo, Feikai Lin, Shuang Zhu, et al.
PLoS ONE (2019) Vol. 14, Iss. 4, pp. e0215134-e0215134
Open Access | Times Cited: 87
Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India
Anik Saha, Sunil Saha
Remote Sensing Applications Society and Environment (2020) Vol. 19, pp. 100323-100323
Closed Access | Times Cited: 72
Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models
Zhou Zhao, Zeng yuan Liu, Chong Xu
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 66
Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area
Chaofei He, Fulong Chen, Aihua Long, et al.
Agricultural Water Management (2023) Vol. 279, pp. 108161-108161
Open Access | Times Cited: 33
Random forest algorithm and support vector machine for nondestructive assessment of mass moisture content of brick walls in historic buildings
Anna Hoła, Sławomir Czarnecki
Automation in Construction (2023) Vol. 149, pp. 104793-104793
Closed Access | Times Cited: 32
Assessment of infrastructure resilience in multi-hazard regions: A case study of Khuzestan Province
Mahmoud Arvin, Parisa Beiki, Seyed Jafar Hejazi, et al.
International Journal of Disaster Risk Reduction (2023) Vol. 88, pp. 103601-103601
Closed Access | Times Cited: 26
Predicting slope stability potential failure surface using machine learning algorithms
Myoung-Soo Won, Shamsher Sadiq, Jianbin Wang, et al.
Arabian Journal of Geosciences (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping
Omid Ghorbanzadeh, Bakhtiar Feizizadeh, Thomas Blaschke
Environmental Earth Sciences (2018) Vol. 77, Iss. 16
Closed Access | Times Cited: 79
A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment
Binh Thai Pham, Indra Prakash
Bulletin of Engineering Geology and the Environment (2017) Vol. 78, Iss. 3, pp. 1911-1925
Closed Access | Times Cited: 72
Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea
Sunmin Lee, Moung-Jin Lee, Hyung-Sup Jung, et al.
Geocarto International (2019) Vol. 35, Iss. 15, pp. 1665-1679
Closed Access | Times Cited: 72
A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET
Hongquan Guo, Hoang Nguyen, Xuan-Nam Bui, et al.
Engineering With Computers (2019) Vol. 37, Iss. 1, pp. 421-435
Closed Access | Times Cited: 69
Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms
Vali Vakhshoori, Hamid Reza Pourghasemi, Mohammad Amin Zare, et al.
Water (2019) Vol. 11, Iss. 11, pp. 2292-2292
Open Access | Times Cited: 69
Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh
Bayes Ahmed, Md. Shahinoor Rahman, Rahenul Islam, et al.
ISPRS International Journal of Geo-Information (2018) Vol. 7, Iss. 12, pp. 485-485
Open Access | Times Cited: 68
Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China
Wei Chen, Limin Fan, Cheng Li, et al.
Applied Sciences (2019) Vol. 10, Iss. 1, pp. 29-29
Open Access | Times Cited: 66
Mapping Landslide Susceptibility Using Machine Learning Algorithms and GIS: A Case Study in Shexian County, Anhui Province, China
Zitao Wang, Qimeng Liu, Yu Liu
Symmetry (2020) Vol. 12, Iss. 12, pp. 1954-1954
Open Access | Times Cited: 64
An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia
Mutiara Syifa, Prima Riza Kadavi, Chang-Wook Lee
Sensors (2019) Vol. 19, Iss. 3, pp. 542-542
Open Access | Times Cited: 63
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