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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:

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

Showing 1-25 of 437 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
Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks
Shunping Ji, Dawen Yu, Chaoyong Shen, et al.
Landslides (2020) Vol. 17, Iss. 6, pp. 1337-1352
Closed Access | Times Cited: 281
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
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
GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan
Kashif Ullah, Jiquan Zhang
PLoS ONE (2020) Vol. 15, Iss. 3, pp. e0229153-e0229153
Open Access | Times Cited: 216
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 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: 206
Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm
Ayaz Ahmad, Furqan Farooq, Paweł Niewiadomski, et al.
Materials (2021) Vol. 14, Iss. 4, pp. 794-794
Open Access | Times Cited: 206
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: 195
Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management
Zizheng Guo, Yu Shi, Faming Huang, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 6, pp. 101249-101249
Open Access | Times Cited: 180
A Survey on ensemble learning under the era of deep learning
Yongquan Yang, Haijun Lv, Ning Chen
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 5545-5589
Closed Access | Times Cited: 162
Threats of climate and land use change on future flood susceptibility
Paramita Roy, Subodh Chandra Pal, Rabin Chakrabortty, et al.
Journal of Cleaner Production (2020) Vol. 272, pp. 122757-122757
Closed Access | Times Cited: 160
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
Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms
Mahdi Panahi, Amiya Gayen, Hamid Reza Pourghasemi, et al.
The Science of The Total Environment (2020) Vol. 741, pp. 139937-139937
Closed Access | Times Cited: 155
Brain-Computer Interface: Advancement and Challenges
M. F. Mridha, Sujoy Chandra Das, Md. Mohsin Kabir, et al.
Sensors (2021) Vol. 21, Iss. 17, pp. 5746-5746
Open Access | Times Cited: 155
Machine learning for landslides prevention: a survey
Zhengjing Ma, Gang Mei, Francesco Piccialli
Neural Computing and Applications (2020) Vol. 33, Iss. 17, pp. 10881-10907
Open Access | Times Cited: 150
Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions
Faming Huang, Jun Yan, Xuanmei Fan, et al.
Geoscience Frontiers (2021) Vol. 13, Iss. 2, pp. 101317-101317
Open Access | Times Cited: 141
A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping
Deliang Sun, Shuxian Shi, Haijia Wen, et al.
Geomorphology (2021) Vol. 379, pp. 107623-107623
Closed Access | Times Cited: 128
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra
Expert Systems with Applications (2023) Vol. 244, pp. 122778-122778
Open Access | Times Cited: 128
Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
Swapan Talukdar, Kutub Uddin Eibek, Shumona Akhter, et al.
Ecological Indicators (2021) Vol. 126, pp. 107612-107612
Open Access | Times Cited: 124
A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping
Liang Lv, Tao Chen, Jie Dou, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102713-102713
Open Access | Times Cited: 119
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
Can Yang, Leilei Liu, Faming Huang, et al.
Gondwana Research (2022) Vol. 123, pp. 198-216
Closed Access | Times Cited: 94
Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization
Deliang Sun, Qingyu Gu, Haijia Wen, et al.
Gondwana Research (2022) Vol. 123, pp. 89-106
Closed Access | Times Cited: 94
Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area
Chao Zhou, Ying Cao, Xie Hu, et al.
Landslides (2022) Vol. 19, Iss. 7, pp. 1585-1597
Closed Access | Times Cited: 92
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