Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Showing 1-25 of 77 citing articles:

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
Songlin Liu, Luqi Wang, Wengang Zhang, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2283-2301
Closed Access | Times Cited: 114
Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3029-3029
Open Access | Times Cited: 109
Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)
Taşkın Kavzoğlu, Alihan Teke
Bulletin of Engineering Geology and the Environment (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 84
Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms
Swapan Talukdar, Shahfahad, Shakeel Ahmed, et al.
Journal of Cleaner Production (2023) Vol. 406, pp. 136885-136885
Closed Access | Times Cited: 63
Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency
Yi Zhu, Wen Xu, Wenhong Luo, et al.
Energy (2025), pp. 133581-133581
Closed Access | Times Cited: 6
Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping
Wang Shibao, Jianqi Zhuang, Jia Zheng, et al.
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 75
Mapping China’s Forest Fire Risks with Machine Learning
Yakui Shao, Zhongke Feng, Linhao Sun, et al.
Forests (2022) Vol. 13, Iss. 6, pp. 856-856
Open Access | Times Cited: 52
Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm
Duong Tran Anh, Manish Pandey, Varun Narayan Mishra, et al.
Applied Soft Computing (2022) Vol. 132, pp. 109848-109848
Closed Access | Times Cited: 52
Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China
Chaoxue Tan, Zhongke Feng
Sustainability (2023) Vol. 15, Iss. 7, pp. 6292-6292
Open Access | Times Cited: 27
Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods
Yong Jie Wong, Ali Yeganeh, Min Yan Chia, et al.
Atmospheric Environment (2023) Vol. 301, pp. 119677-119677
Open Access | Times Cited: 26
Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost
Na Lin, Di Zhang, Shanshan Feng, et al.
Remote Sensing (2023) Vol. 15, Iss. 15, pp. 3901-3901
Open Access | Times Cited: 25
Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm
Yuan Cao, F. Y. Su, Maxwell Fordjour Antwi-Afari, et al.
Journal of Cleaner Production (2024) Vol. 465, pp. 142746-142746
Closed Access | Times Cited: 17
Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network
Long Chai, Xiongyao Xie, Cheng Wang, et al.
International Journal of Digital Earth (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 16
Data-driven analysis in 3D concrete printing: predicting and optimizing construction mixtures
Rodrigo Teixeira Schossler, Shafi Ullah, Zaid Alajlan, et al.
AI in Civil Engineering (2025) Vol. 4, Iss. 1
Open Access | Times Cited: 1
Investigating the efficacy of physics-based metaheuristic algorithms in combination with explainable ensemble machine-learning models for landslide susceptibility mapping
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Rizwan Ali Naqvi, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access | Times Cited: 1
Bayesian-optimized lithology identification via visible and near-infrared spectral data analysis
Zhenhao Xu, Shan Li, Peng Lin, et al.
Intelligent geoengineering. (2025)
Open Access | Times Cited: 1
Coupling Interpretable Feature Selection with Machine Learning for Evapotranspiration Gap Filling
Lizheng Wang, Lixin Dong, Qiutong Zhang
Water (2025) Vol. 17, Iss. 5, pp. 748-748
Open Access | Times Cited: 1
Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
Guangzhi Rong, Kaiwei Li, Yulin Su, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4694-4694
Open Access | Times Cited: 47
Permafrost degradation induced thaw settlement susceptibility research and potential risk analysis in the Qinghai-Tibet Plateau
Renwei Li, Mingyi Zhang, Pavel Konstantinov, et al.
CATENA (2022) Vol. 214, pp. 106239-106239
Closed Access | Times Cited: 31
Stability prediction of underground entry-type excavations based on particle swarm optimization and gradient boosting decision tree
Jian Zhou, Shuai Huang, Ming Tao, et al.
Underground Space (2022) Vol. 9, pp. 234-249
Open Access | Times Cited: 29
A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys
Junyu Guo, Xueping Zan, Lin Wang, et al.
Engineering Fracture Mechanics (2023) Vol. 293, pp. 109714-109714
Closed Access | Times Cited: 23
Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance
Li Zhuo, Yupu Huang, Jing Zheng, et al.
Sustainability (2023) Vol. 15, Iss. 11, pp. 9024-9024
Open Access | Times Cited: 17
A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4521-4521
Open Access | Times Cited: 36
Estimation of rainfall erosivity factor in Italy and Switzerland using Bayesian optimization based machine learning models
Seoro Lee, Joo Hyun Bae, Jiyeong Hong, et al.
CATENA (2021) Vol. 211, pp. 105957-105957
Open Access | Times Cited: 36
Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results
Jiahui Dong, Ruiqing Niu, Bingquan Li, et al.
Geomatics Natural Hazards and Risk (2022) Vol. 14, Iss. 1, pp. 52-75
Open Access | Times Cited: 28
Page 1 - Next Page

Scroll to top