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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:

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)
Binh Thai Pham, Biswajeet Pradhan, Dieu Tien Bui, et al.
Environmental Modelling & Software (2016) Vol. 84, pp. 240-250
Closed Access | Times Cited: 486

Showing 1-25 of 486 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: 823
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
Khabat Khosravi, Binh Thai Pham, Kamran Chapi, et al.
The Science of The Total Environment (2018) Vol. 627, pp. 744-755
Closed Access | Times Cited: 653
Review on landslide susceptibility mapping using support vector machines
Yu Huang, Lu Zhao
CATENA (2018) Vol. 165, pp. 520-529
Closed Access | Times Cited: 587
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
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
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Haoyuan Hong, Junzhi Liu, Dieu Tien Bui, et al.
CATENA (2018) Vol. 163, pp. 399-413
Closed Access | Times Cited: 462
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
Prediction of the landslide susceptibility: Which algorithm, which precision?
Hamid Reza Pourghasemi, Omid Rahmati
CATENA (2017) Vol. 162, pp. 177-192
Closed Access | Times Cited: 424
Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China
Chao Zhou, Kunlong Yin, Ying Cao, et al.
Computers & Geosciences (2017) Vol. 112, pp. 23-37
Open Access | Times Cited: 365
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
Ahmed M. Youssef, Hamid Reza Pourghasemi
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 639-655
Open Access | Times Cited: 336
Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping
Hossein Shafizadeh‐Moghadam, Roozbeh Valavi, Himan Shahabi, et al.
Journal of Environmental Management (2018) Vol. 217, pp. 1-11
Open Access | Times Cited: 327
Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization
Xinzhi Zhou, Haijia Wen, Yalan Zhang, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101211-101211
Open Access | Times Cited: 303
Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques
Mahyat Shafapour Tehrany, Simon Jones, Farzin Shabani
CATENA (2018) Vol. 175, pp. 174-192
Closed Access | Times Cited: 299
Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
Maher Ibrahim Sameen, Biswajeet Pradhan, Saro Lee
CATENA (2019) Vol. 186, pp. 104249-104249
Open Access | Times Cited: 296
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
Dong Van Dao, Abolfazl Jaafari, Mahmoud Bayat, et al.
CATENA (2020) Vol. 188, pp. 104451-104451
Closed Access | Times Cited: 292
GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks
Dieu Tien Bui, Tien-Chung Ho, Biswajeet Pradhan, et al.
Environmental Earth Sciences (2016) Vol. 75, Iss. 14
Closed Access | Times Cited: 291
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
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Zhice Fang, Yi Wang, Ling Peng, et al.
Computers & Geosciences (2020) Vol. 139, pp. 104470-104470
Closed Access | Times Cited: 268
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Emrehan Kutluğ Şahin
SN Applied Sciences (2020) Vol. 2, Iss. 7
Open Access | Times Cited: 258
Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques
Wei Chen, Hamid Reza Pourghasemi, Mahdi Panahi, et al.
Geomorphology (2017) Vol. 297, pp. 69-85
Closed Access | Times Cited: 257
GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method
Wei Chen, Xiaoshen Xie, Jianbing Peng, et al.
CATENA (2018) Vol. 164, pp. 135-149
Open Access | Times Cited: 251
Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study
Hariklia D. Skilodimou, George D. Bathrellos, Konstantinos Chousianitis, et al.
Environmental Earth Sciences (2019) Vol. 78, Iss. 2
Closed Access | Times Cited: 247
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
Wei Chen, Mahdi Panahi, Paraskevas Tsangaratos, et al.
CATENA (2018) Vol. 172, pp. 212-231
Closed Access | Times Cited: 243
Review on remote sensing methods for landslide detection using machine and deep learning
Amrita Mohan, Amit Kumar Singh, Basant Kumar, et al.
Transactions on Emerging Telecommunications Technologies (2020) Vol. 32, Iss. 7
Closed Access | Times Cited: 238
Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility
Abolfazl Jaafari, Mahdi Panahi, Binh Thai Pham, et al.
CATENA (2019) Vol. 175, pp. 430-445
Closed Access | Times Cited: 236
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