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:

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping
Omid Ghorbanzadeh, Hashem Rostamzadeh, Thomas Blaschke, et al.
Natural Hazards (2018) Vol. 94, Iss. 2, pp. 497-517
Open Access | Times Cited: 110

Showing 1-25 of 110 citing articles:

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Omid Ghorbanzadeh, Thomas Blaschke, Khalil Gholamnia, et al.
Remote Sensing (2019) Vol. 11, Iss. 2, pp. 196-196
Open Access | Times Cited: 707
Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches
Omid Ghorbanzadeh, Khalil Valizadeh Kamran, Thomas Blaschke, et al.
Fire (2019) Vol. 2, Iss. 3, pp. 43-43
Open Access | Times Cited: 165
Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis
Binh Thai Pham, Manh Duc Nguyen, Dong Van Dao, et al.
The Science of The Total Environment (2019) Vol. 679, pp. 172-184
Closed Access | Times Cited: 150
State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction
S.C. Jong, Dominic Ek Leong Ong, Erwin Oh
Tunnelling and Underground Space Technology (2021) Vol. 113, pp. 103946-103946
Closed Access | Times Cited: 114
A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions
Sepideh Tavakkoli Piralilou, Golzar Einali, Omid Ghorbanzadeh, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 672-672
Open Access | Times Cited: 82
Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables
Omid Ghorbanzadeh, Thomas Blaschke, Khalil Gholamnia, et al.
Fire (2019) Vol. 2, Iss. 3, pp. 50-50
Open Access | Times Cited: 146
Land subsidence modelling using tree-based machine learning algorithms
Omid Rahmati, Fatemeh Falah, Seyed Amir Naghibi, et al.
The Science of The Total Environment (2019) Vol. 672, pp. 239-252
Closed Access | Times Cited: 141
Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions
Sahar Abdollahi, Hamid Reza Pourghasemi, Gholamabbas Ghanbarian, et al.
Bulletin of Engineering Geology and the Environment (2018) Vol. 78, Iss. 6, pp. 4017-4034
Closed Access | Times Cited: 136
Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway
Liming Xiao, Yonghong Zhang, Gongzhuang Peng
Sensors (2018) Vol. 18, Iss. 12, pp. 4436-4436
Open Access | Times Cited: 124
Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping
Khalil Gholamnia, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, et al.
Symmetry (2020) Vol. 12, Iss. 4, pp. 604-604
Open Access | Times Cited: 119
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms
Hamid Reza Pourghasemi, Amiya Gayen, Sungjae Park, et al.
Sustainability (2018) Vol. 10, Iss. 10, pp. 3697-3697
Open Access | Times Cited: 109
UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks
Omid Ghorbanzadeh, Sansar Raj Meena, Thomas Blaschke, et al.
Remote Sensing (2019) Vol. 11, Iss. 17, pp. 2046-2046
Open Access | Times Cited: 108
Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method
Sunil Saha, Alireza Arabameri, Anik Saha, et al.
The Science of The Total Environment (2020) Vol. 764, pp. 142928-142928
Closed Access | Times Cited: 106
A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal
Sansar Raj Meena, Omid Ghorbanzadeh, Thomas Blaschke
ISPRS International Journal of Geo-Information (2019) Vol. 8, Iss. 2, pp. 94-94
Open Access | Times Cited: 96
Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach
Sansar Raj Meena, Omid Ghorbanzadeh, C.J. van Westen, et al.
Landslides (2021) Vol. 18, Iss. 5, pp. 1937-1950
Open Access | Times Cited: 94
A review on flood management technologies related to image processing and machine learning
Hafiz Suliman Munawar, Ahmed W. A. Hammad, S. Travis Waller
Automation in Construction (2021) Vol. 132, pp. 103916-103916
Closed Access | Times Cited: 94
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility
Alireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
The Science of The Total Environment (2020) Vol. 726, pp. 138595-138595
Closed Access | Times Cited: 91
GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques
Xia Zhao, Wei Chen
Applied Sciences (2019) Vol. 10, Iss. 1, pp. 16-16
Open Access | Times Cited: 90
Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments
Wei Chen, Yunzhi Chen, Paraskevas Tsangaratos, et al.
Remote Sensing (2020) Vol. 12, Iss. 23, pp. 3854-3854
Open Access | Times Cited: 86
Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
Mohammad Mehrabi, Biswajeet Pradhan, Hossein Moayedi, et al.
Sensors (2020) Vol. 20, Iss. 6, pp. 1723-1723
Open Access | Times Cited: 81
Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria
Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Khalil Gholamnia, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2757-2757
Open Access | Times Cited: 80
A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia
Hejar Shahabi, Ben Jarihani, Sepideh Tavakkoli Piralilou, et al.
Sensors (2019) Vol. 19, Iss. 22, pp. 4893-4893
Open Access | Times Cited: 79
Earthquake Prediction Using Expert Systems: A Systematic Mapping Study
Rabia Tehseen, Muhammad Shoaib Farooq, Adnan Abid
Sustainability (2020) Vol. 12, Iss. 6, pp. 2420-2420
Open Access | Times Cited: 75
Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, et al.
Natural hazards and earth system sciences (2022) Vol. 22, Iss. 4, pp. 1395-1417
Open Access | Times Cited: 48
Role of Artificial Intelligence in Future of Education
Roopal Shrivastava
International Journal of Professional Business Review (2023) Vol. 8, Iss. 1, pp. e0840-e0840
Open Access | Times Cited: 27
Page 1 - Next Page

Scroll to top