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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:

Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost
Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2022) Vol. 37, Iss. 3, pp. 1067-1092
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
Nafees Ali, Jian Chen, Xiaodong Fu, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 988-988
Open Access | Times Cited: 14
Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification
Sumon Dey, Swarup Das
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 13
Effect of landslide spatial representation and raster resolution on the landslide susceptibility assessment
Shuo Yang, Deying Li, Yiqing Sun, et al.
Environmental Earth Sciences (2024) Vol. 83, Iss. 4
Open Access | Times Cited: 9
Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China
Zhaowei Yao, Meihong Chen, Jiewei Zhan, et al.
Applied Sciences (2023) Vol. 13, Iss. 23, pp. 12817-12817
Open Access | Times Cited: 16
XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy
Qiaoyun Wang, Xin Zou, Yinji Chen, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 323, pp. 124917-124917
Closed Access | Times Cited: 6
What ecological factors to integrate in landslide susceptibility mapping? An exploratory review of current trends in support of eco-DRR
Mélanie Broquet, Pedro Cabral, Felipe S. Campos
Progress in Disaster Science (2024) Vol. 22, pp. 100328-100328
Open Access | Times Cited: 5
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
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
Paul Esteban Bravo-López, Tomás Fernández, Chester Sellers, et al.
Land (2023) Vol. 12, Iss. 6, pp. 1135-1135
Open Access | Times Cited: 13
Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway
Jiao Wu, Ya Zhang, Liu Yang, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 45, pp. 100675-100700
Closed Access | Times Cited: 11
Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake
Siyuan Ma, Xiaoyi Shao, Chong Xu
Remote Sensing (2023) Vol. 15, Iss. 19, pp. 4733-4733
Open Access | Times Cited: 10
The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction
Selçuk Demir, Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 8, pp. 3273-3290
Open Access | Times Cited: 3
Application of state-of-the-art machine learning algorithms for slope stability prediction by handling outliers of the dataset
Selçuk Demir, Emrehan Kutluğ Şahin
Earth Science Informatics (2023) Vol. 16, Iss. 3, pp. 2497-2509
Closed Access | Times Cited: 9
A novel landslide susceptibility prediction framework based on contrastive loss
Shubing Ouyang, Weitao Chen, Hangyuan Liu, et al.
GIScience & Remote Sensing (2024) Vol. 61, Iss. 1
Open Access | Times Cited: 3
Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis
Shilong Yang, Jiayao Tan, Danyuan Luo, et al.
Computers & Geosciences (2024), pp. 105723-105723
Closed Access | Times Cited: 2
Assessing the Dimensionality Reduction of the Geospatial Dataset Using Principal Component Analysis (PCA) and Its Impact on the Accuracy and Performance of Ensembled and Non-ensembled Algorithms
Farkhanda Abbas, Feng Zhang, Javed Iqbal, et al.
(2023)
Open Access | Times Cited: 6
Markov–Switching Spatio–Temporal Generalized Additive Model for Landslide Susceptibility
A. Sridharan, Georg Gutjahr, Sundararaman Gopalan
Environmental Modelling & Software (2023) Vol. 173, pp. 105892-105892
Closed Access | Times Cited: 5
Landslide Susceptibility Mapping Through Hyperparameter Optimized Bagging and Boosting Ensembles: Case Study of NH-10, West Bengal, India
Sumon Dey, Swarup Das
Advances in geographic information science (2024), pp. 123-140
Closed Access | Times Cited: 1
Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India
Md Hasanuzzaman, Pravat Kumar Shit, Saeed Alqadhi, et al.
Sustainability (2024) Vol. 16, Iss. 15, pp. 6569-6569
Open Access | Times Cited: 1
Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions
Yi Han, Shabnam J. Semnani
Acta Geotechnica (2024)
Open Access | Times Cited: 1
An integrated approach of support vector machine (SVM) and weight of evidence (WOE) techniques to map groundwater potential and assess water quality
Malik Talha Riaz, Muhammad Tayyib Riaz, Adnanul Rehman, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Assessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability prediction
Selçuk Demir, Emrehan Kutluğ Şahin
Environmental Earth Sciences (2023) Vol. 82, Iss. 23
Closed Access | Times Cited: 4
IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT
Sefri Imanuel Fallo, Lidia Paskalia Nipu
BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN (2024) Vol. 18, Iss. 2, pp. 0877-0892
Open Access
A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP
Şevket Bediroğlu
Karadeniz Fen Bilimleri Dergisi (2024) Vol. 14, Iss. 3, pp. 1204-1224
Open Access
A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine
Yuwei Liu, Yong Xu, Han Hu, et al.
Frontiers in Earth Science (2024) Vol. 12
Open Access
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