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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:

Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data
Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1737-1737
Open Access | Times Cited: 130

Showing 1-25 of 130 citing articles:

Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101075-101075
Open Access | Times Cited: 430
Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Taşkın Kavzoğlu, Alihan Teke
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 6, pp. 7367-7385
Closed Access | Times Cited: 198
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen
Applied Sciences (2022) Vol. 12, Iss. 17, pp. 8654-8654
Open Access | Times Cited: 190
Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal, et al.
Sensors (2020) Vol. 20, Iss. 19, pp. 5609-5609
Open Access | Times Cited: 147
Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review
Rami Al‐Ruzouq, Mohamed Barakat A. Gibril, Abdallah Shanableh, et al.
Remote Sensing (2020) Vol. 12, Iss. 20, pp. 3338-3338
Open Access | Times Cited: 146
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities
Wei Han, Xiaohan Zhang, Yi Wang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 202, pp. 87-113
Closed Access | Times Cited: 122
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
Landslide Susceptibility Assessment by Using Convolutional Neural Network
Shahrzad Nikoobakht, Mohammad Azarafza, Haluk Akgün, et al.
Applied Sciences (2022) Vol. 12, Iss. 12, pp. 5992-5992
Open Access | Times Cited: 98
A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
Husam A. H. Al-Najjar, Biswajeet Pradhan, Ghassan Beydoun, et al.
Gondwana Research (2022) Vol. 123, pp. 107-124
Closed Access | Times Cited: 96
Multi-hazard susceptibility mapping based on Convolutional Neural Networks
Kashif Ullah, Yi Wang, Zhice Fang, et al.
Geoscience Frontiers (2022) Vol. 13, Iss. 5, pp. 101425-101425
Open Access | Times Cited: 91
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: 85
GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya
Suvam Das, Shantanu Sarkar, Debi Prasanna Kanungo
Environmental Monitoring and Assessment (2022) Vol. 194, Iss. 4
Closed Access | Times Cited: 81
Optimizing landslide susceptibility mapping using machine learning and geospatial techniques
Gazali Agboola, Leila Hashemi-Beni, Tamer Elbayoumi, et al.
Ecological Informatics (2024) Vol. 81, pp. 102583-102583
Open Access | Times Cited: 28
Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities
Qi Zhang, Teng Wang
Remote Sensing (2024) Vol. 16, Iss. 8, pp. 1344-1344
Open Access | Times Cited: 25
Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data
Bahareh Kalantar, Naonori Ueda, Mohammed Oludare Idrees, et al.
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3682-3682
Open Access | Times Cited: 139
Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping
Mohammed Sarfaraz Gani Adnan, Md Salman Rahman, Nahian Ahmed, et al.
Remote Sensing (2020) Vol. 12, Iss. 20, pp. 3347-3347
Open Access | Times Cited: 117
Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
Yunzhi Chen, Wei Chen, Subodh Chandra Pal, et al.
Geocarto International (2021) Vol. 37, Iss. 19, pp. 5564-5584
Closed Access | Times Cited: 97
Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh
Mahfuzur Rahman, Ningsheng Chen, Ahmed Elbeltagi, et al.
Journal of Environmental Management (2021) Vol. 295, pp. 113086-113086
Closed Access | Times Cited: 89
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
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia
Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, et al.
Remote Sensing (2021) Vol. 13, Iss. 13, pp. 2638-2638
Open Access | Times Cited: 73
Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia
Leulalem Shano, Tarun Kumar Raghuvanshi, Matebie Meten
Arabian Journal of Geosciences (2021) Vol. 14, Iss. 7
Closed Access | Times Cited: 70
A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan
Naeem Shahzad, Xiaoli Ding, Sawaid Abbas
Applied Sciences (2022) Vol. 12, Iss. 5, pp. 2280-2280
Open Access | Times Cited: 42
Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia
Siti Norsakinah Selamat, Nuriah Abd Majid, Mohd Raihan Taha, et al.
Land (2022) Vol. 11, Iss. 6, pp. 833-833
Open Access | Times Cited: 42
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
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
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