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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.

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Showing 1-25 of 398 citing articles:

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
Wei Chen, Xiaoshen Xie, Jiale Wang, et al.
CATENA (2016) Vol. 151, pp. 147-160
Open Access | Times Cited: 808
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
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
Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
Yi Wang, Zhice Fang, Haoyuan Hong
The Science of The Total Environment (2019) Vol. 666, pp. 975-993
Closed Access | Times Cited: 436
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
Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling
Wei Chen, Shuai Zhang, Renwei Li, et al.
The Science of The Total Environment (2018) Vol. 644, pp. 1006-1018
Closed Access | Times Cited: 412
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
Dieu Tien Bui, Paraskevas Tsangaratos, Viet-Tien Nguyen, et al.
CATENA (2020) Vol. 188, pp. 104426-104426
Closed Access | Times Cited: 390
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
Faming Huang, Zhongshan Cao, Jianfei Guo, et al.
CATENA (2020) Vol. 191, pp. 104580-104580
Closed Access | Times Cited: 389
Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
Wei Chen, Hamid Reza Pourghasemi, Aiding Kornejady, et al.
Geoderma (2017) Vol. 305, pp. 314-327
Closed Access | Times Cited: 362
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
Yanli Wu, Yutian Ke, Zhuo Chen, et al.
CATENA (2019) Vol. 187, pp. 104396-104396
Open Access | Times Cited: 347
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
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
Shallow landslide susceptibility assessment using a novel hybrid intelligence approach
Ataollah Shirzadi, Dieu Tien Bui, Binh Thai Pham, et al.
Environmental Earth Sciences (2017) Vol. 76, Iss. 2
Closed Access | Times Cited: 252
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
GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods
Xi Chen, Wei Chen
CATENA (2020) Vol. 196, pp. 104833-104833
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
Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques
Amina Khan, Sumeet Gupta, Sachin Kumar Gupta
International Journal of Disaster Risk Reduction (2020) Vol. 47, pp. 101642-101642
Closed Access | Times Cited: 240
Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016)
Hamid Reza Pourghasemi, Zeinab Teimoori Yansari, Panos Panagos, et al.
Arabian Journal of Geosciences (2018) Vol. 11, Iss. 9
Closed Access | Times Cited: 224
Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees
Binh Thai Pham, Indra Prakash, Dieu Tien Bui
Geomorphology (2017) Vol. 303, pp. 256-270
Closed Access | Times Cited: 220
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
Qingfeng He, Himan Shahabi, Ataollah Shirzadi, et al.
The Science of The Total Environment (2019) Vol. 663, pp. 1-15
Closed Access | Times Cited: 220
A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling
Binh Thai Pham, Abolfazl Jaafari, Indra Prakash, et al.
Bulletin of Engineering Geology and the Environment (2018) Vol. 78, Iss. 4, pp. 2865-2886
Closed Access | Times Cited: 219
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
Zhice Fang, Yi Wang, Ling Peng, et al.
International Journal of Geographical Information Science (2020) Vol. 35, Iss. 2, pp. 321-347
Open Access | Times Cited: 211
Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability
Mariano Di Napoli, Francesco Carotenuto, Andrea Cevasco, et al.
Landslides (2020) Vol. 17, Iss. 8, pp. 1897-1914
Closed Access | Times Cited: 208
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
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