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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:

A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers
Binh Thai Pham, Indra Prakash, Jie Dou, et al.
Geocarto International (2018) Vol. 35, Iss. 12, pp. 1267-1292
Closed Access | Times Cited: 156

Showing 1-25 of 156 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
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
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
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 266-266
Open Access | Times Cited: 301
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
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
Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
Abhirup Dikshit, Raju Sarkar, Biswajeet Pradhan, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2466-2466
Open Access | Times Cited: 234
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
Comparative study of landslide susceptibility mapping with different recurrent neural networks
Yi Wang, Zhice Fang, Mao Wang, et al.
Computers & Geosciences (2020) Vol. 138, pp. 104445-104445
Closed Access | Times Cited: 230
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
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
GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment
Binh Thai Pham, Mohammadtaghi Avand, Saeid Janizadeh, et al.
Water (2020) Vol. 12, Iss. 3, pp. 683-683
Open Access | Times Cited: 207
Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Yaning Yi, Zhijie Zhang, Wanchang Zhang, et al.
CATENA (2020) Vol. 195, pp. 104851-104851
Closed Access | Times Cited: 196
Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
Haoyuan Hong, Junzhi Liu, A‐Xing Zhu
The Science of The Total Environment (2020) Vol. 718, pp. 137231-137231
Closed Access | Times Cited: 176
Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms
Binh Thai Pham, Ataollah Shirzadi, Himan Shahabi, et al.
Sustainability (2019) Vol. 11, Iss. 16, pp. 4386-4386
Open Access | Times Cited: 168
Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
Phong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2469-2469
Open Access | Times Cited: 155
Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM
Jie Dou, Ali P. Yunus, Dieu Tien Bui, et al.
Remote Sensing (2019) Vol. 11, Iss. 6, pp. 638-638
Open Access | Times Cited: 147
Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India
Kanu Mandal, Sunil Saha, Sujit Mandal
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101203-101203
Open Access | Times Cited: 131
Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
Zhilu Chang, Filippo Catani, Faming Huang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 5, pp. 1127-1143
Open Access | Times Cited: 127
An identification method of potential landslide zones using InSAR data and landslide susceptibility
Yi He, Wenhui Wang, Lifeng Zhang, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 44
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Binh Thai Pham, T. Nguyen‐Thoi, Chongchong Qi, et al.
CATENA (2020) Vol. 195, pp. 104805-104805
Closed Access | Times Cited: 136
Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Himan Shahabi, Ataollah Shirzadi, Somayeh Ronoud, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101100-101100
Open Access | Times Cited: 133
Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models
Tao Chen, Li Zhu, Ruiqing Niu, et al.
Journal of Mountain Science (2020) Vol. 17, Iss. 3, pp. 670-685
Closed Access | Times Cited: 126
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