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:

Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Mahdis Amiri, et al.
Natural Hazards (2021) Vol. 108, Iss. 1, pp. 1291-1316
Closed Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey
Hazan Alkan Akıncı, Halil Akıncı
Earth Science Informatics (2023) Vol. 16, Iss. 1, pp. 397-414
Closed Access | Times Cited: 53

A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science
Abdul‐Lateef Balogun, Abdulwaheed Tella, Lavania Baloo, et al.
Urban Climate (2021) Vol. 40, pp. 100989-100989
Closed Access | Times Cited: 87

Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards
Ömer Ekmekcioğlu, Kerim Koç
CATENA (2022) Vol. 216, pp. 106379-106379
Closed Access | Times Cited: 44

An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru
Chandan Kumar, Gabriel Walton, Paul M. Santi, et al.
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1376-1376
Open Access | Times Cited: 31

Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey
Ayşe Yavuz Özalp, Halil Akıncı, Mustafa Zeybek
Water (2023) Vol. 15, Iss. 14, pp. 2661-2661
Open Access | Times Cited: 29

Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution
Babitha Ganesh, Shweta Vincent, Sameena Pathan, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 29, pp. 100905-100905
Closed Access | Times Cited: 26

Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction
Chandan Kumar, Gabriel Walton, Paul M. Santi, et al.
Remote Sensing (2025) Vol. 17, Iss. 2, pp. 213-213
Open Access | Times Cited: 1

Geospatial Analysis of Landslide Susceptibility and Safe Relocation Zones: Insights from Recent Disasters in Gofa Zone, Southern Ethiopia
Natnael Agegnehu Ayele, Esubalew Mulugeta Engda, Taye Teshome Terefe, et al.
Quaternary Science Advances (2025), pp. 100272-100272
Open Access | Times Cited: 1

Influence of anthropogenic activities on landslide susceptibility: A case study in Solan district, Himachal Pradesh, India
Sangeeta, Sanjay Kumar Singh
Journal of Mountain Science (2023) Vol. 20, Iss. 2, pp. 429-447
Closed Access | Times Cited: 21

Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya
Sunil Saha, Anik Saha, Tusar Kanti Hembram, et al.
Geocarto International (2022) Vol. 37, Iss. 27, pp. 17018-17043
Closed Access | Times Cited: 23

Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam
Quan Vu Viet Du, Huu Duy Nguyen, Viet Thanh Pham, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 15

Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions
Quoc Bao Pham, Ömer Ekmekcioğlu, Sk Ajim Ali, et al.
Applied Soft Computing (2023) Vol. 143, pp. 110429-110429
Closed Access | Times Cited: 12

Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, et al.
Natural hazards and earth system sciences (2025) Vol. 25, Iss. 1, pp. 183-206
Open Access

Machine Learning-assisted Investigation of Landslide Susceptibility for Aglar Watershed in the Lesser Himalaya Region
Dipika Keshri, Shovan Lal Chattoraj, Rakesh Kumar Pandey, et al.
Journal of the Geological Society of India (2025) Vol. 101, Iss. 3, pp. 384-396
Closed Access

Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China
Bofan Yu, Hui Li, Huaixue Xing, et al.
Geomatics Natural Hazards and Risk (2025) Vol. 16, Iss. 1
Open Access

Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math
Xianyu Yu, Yang Xia, Jianguo Zhou, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 2226-2226
Open Access | Times Cited: 9

Analysis of deformation mechanism of rainfall-induced landslide in the Three Gorges Reservoir Area: Piansongshu landslide
Hui Wang, Jianhua Zou, Xinghua Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3

Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling
Muhammad Tayyib Riaz, Muhammad Basharat, Quoc Bao Pham, et al.
Geocarto International (2022) Vol. 37, Iss. 26, pp. 12294-12337
Closed Access | Times Cited: 13

Raster-based landslide susceptibility mapping using compensatory MADM methods
Amin Salehpour Jam, Jamal Mosaffaie, Mahmoud Reza Tabatabaei
Environmental Modelling & Software (2022) Vol. 159, pp. 105567-105567
Closed Access | Times Cited: 13

Slope-scale landslide susceptibility assessment based on coupled models of frequency ratio and multiple regression analysis with limited historical hazards data
Jianfeng Sun, Tiesheng Yan, Jinshu Hu, et al.
Natural Hazards (2023) Vol. 120, Iss. 1, pp. 1-23
Closed Access | Times Cited: 8

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