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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:

An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan
Jie Dou, Hiromitsu Yamagishi, Hamid Reza Pourghasemi, et al.
Natural Hazards (2015) Vol. 78, Iss. 3, pp. 1749-1776
Closed Access | Times Cited: 219

Showing 1-25 of 219 citing articles:

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Abdelaziz Merghadi, Ali P. Yunus, Jie Dou, et al.
Earth-Science Reviews (2020) Vol. 207, pp. 103225-103225
Closed Access | Times Cited: 825
Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
Binh Thai Pham, Dieu Tien Bui, Indra Prakash, et al.
CATENA (2016) Vol. 149, pp. 52-63
Closed Access | Times Cited: 582
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
Khabat Khosravi, Himan Shahabi, Binh Thai Pham, et al.
Journal of Hydrology (2019) Vol. 573, pp. 311-323
Closed Access | Times Cited: 562
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Jie Dou, Ali P. Yunus, Dieu Tien Bui, et al.
The Science of The Total Environment (2019) Vol. 662, pp. 332-346
Closed Access | Times Cited: 509
Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan
Jie Dou, Ali P. Yunus, Dieu Tien Bui, et al.
Landslides (2019) Vol. 17, Iss. 3, pp. 641-658
Closed Access | Times Cited: 437
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
Binh Thai Pham, Dieu Tien Bui, Hamid Reza Pourghasemi, et al.
Theoretical and Applied Climatology (2015) Vol. 128, Iss. 1-2, pp. 255-273
Closed Access | Times Cited: 340
Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling
Hamid Reza Pourghasemi, Saleh Yousefi, Aiding Kornejady, et al.
The Science of The Total Environment (2017) Vol. 609, pp. 764-775
Closed Access | Times Cited: 307
Applications of artificial intelligence for disaster management
Wenjuan Sun, Paolo Bocchini, Brian D. Davison
Natural Hazards (2020) Vol. 103, Iss. 3, pp. 2631-2689
Closed Access | Times Cited: 296
A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning
Lê Thị Lệ, Hoang Nguyen, Jie Dou, et al.
Applied Sciences (2019) Vol. 9, Iss. 13, pp. 2630-2630
Open Access | Times Cited: 286
Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
Binh Thai Pham, Indra Prakash, Sushant K. Singh, et al.
CATENA (2018) Vol. 175, pp. 203-218
Closed Access | Times Cited: 280
Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion
Omid Rahmati, N Tahmasebipour, Ali Haghizadeh, et al.
Geomorphology (2017) Vol. 298, pp. 118-137
Closed Access | Times Cited: 268
Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea
Jeong-Cheol Kim, Sunmin Lee, Hyung-Sup Jung, et al.
Geocarto International (2017) Vol. 33, Iss. 9, pp. 1000-1015
Closed Access | Times Cited: 254
Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning
Jie Dou, Ali P. Yunus, Abdelaziz Merghadi, et al.
The Science of The Total Environment (2020) Vol. 720, pp. 137320-137320
Closed Access | Times Cited: 240
Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
Kuan-Tsung Chang, Abdelaziz Merghadi, Ali P. Yunus, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 231
Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan
Jie Dou, Dieu Tien Bui, Ali P. Yunus, et al.
PLoS ONE (2015) Vol. 10, Iss. 7, pp. e0133262-e0133262
Open Access | Times Cited: 225
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 8, pp. 2749-2749
Open Access | Times Cited: 221
Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy
Haoyuan Hong, Wei Chen, Chong Xu, et al.
Geocarto International (2016), pp. 1-16
Open Access | Times Cited: 216
GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models
Wei Chen, Xiaoshen Xie, Jianbing Peng, et al.
Geomatics Natural Hazards and Risk (2017) Vol. 8, Iss. 2, pp. 950-973
Open Access | Times Cited: 205
GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
Wei Chen, Yang Li
CATENA (2020) Vol. 195, pp. 104777-104777
Closed Access | Times Cited: 204
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
Abolfazl Baghbani, Tanveer Choudhury, Susanga Costa, et al.
Earth-Science Reviews (2022) Vol. 228, pp. 103991-103991
Closed Access | Times Cited: 198
A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China
Wei Chen, Hamid Reza Pourghasemi, Seyed Amir Naghibi
Bulletin of Engineering Geology and the Environment (2017) Vol. 77, Iss. 2, pp. 647-664
Closed Access | Times Cited: 196
Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping
Qingfeng Ding, Wei Chen, Haoyuan Hong
Geocarto International (2016), pp. 1-21
Closed Access | Times Cited: 192
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
Wei Chen, Ataollah Shirzadi, Himan Shahabi, et al.
Geomatics Natural Hazards and Risk (2017) Vol. 8, Iss. 2, pp. 1955-1977
Open Access | Times Cited: 187
Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods
Hamid Reza Pourghasemi, Mauro Rossi
Theoretical and Applied Climatology (2016) Vol. 130, Iss. 1-2, pp. 609-633
Closed Access | Times Cited: 182
A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping
Wei Chen, Hamid Reza Pourghasemi, Zhou Zhao
Geocarto International (2016) Vol. 32, Iss. 4, pp. 367-385
Closed Access | Times Cited: 172
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