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:

Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
Ali Azareh, Omid Rahmati, Elham Rafiei-Sardooi, et al.
The Science of The Total Environment (2018) Vol. 655, pp. 684-696
Open Access | Times Cited: 190

Showing 1-25 of 190 citing articles:

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 266-266
Open Access | Times Cited: 301
Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
Mahdi Panahi, Nitheshnirmal Sãdhasivam, Hamid Reza Pourghasemi, et al.
Journal of Hydrology (2020) Vol. 588, pp. 125033-125033
Closed Access | Times Cited: 290
Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques
Romulus Costache, Quoc Bao Pham, Ehsan Sharifi, et al.
Remote Sensing (2019) Vol. 12, Iss. 1, pp. 106-106
Open Access | Times Cited: 230
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 8, pp. 2749-2749
Open Access | Times Cited: 221
Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion
Younes Garosi, Mohsen Sheklabadi, Christian Conoscenti, et al.
The Science of The Total Environment (2019) Vol. 664, pp. 1117-1132
Closed Access | Times Cited: 189
Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms
Binh Thai Pham, Ataollah Shirzadi, Himan Shahabi, et al.
Sustainability (2019) Vol. 11, Iss. 16, pp. 4386-4386
Open Access | Times Cited: 168
Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model
Duie Tien Bui, Khabat Khosravi, Himan Shahabi, et al.
Remote Sensing (2019) Vol. 11, Iss. 13, pp. 1589-1589
Open Access | Times Cited: 166
Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
Wei Chen, Haoyuan Hong, Mahdi Panahi, et al.
Applied Sciences (2019) Vol. 9, Iss. 18, pp. 3755-3755
Open Access | Times Cited: 154
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility
Wei Chen, Xinxiang Lei, Rabin Chakrabortty, et al.
Journal of Environmental Management (2021) Vol. 284, pp. 112015-112015
Closed Access | Times Cited: 131
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion
Hamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
The Science of The Total Environment (2023) Vol. 904, pp. 166960-166960
Closed Access | Times Cited: 48
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
Viet‐Ha Nhu, Ayub Mohammadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 14, pp. 4933-4933
Open Access | Times Cited: 121
GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran
Xinxiang Lei, Wei Chen, Mohammadtaghi Avand, et al.
Remote Sensing (2020) Vol. 12, Iss. 15, pp. 2478-2478
Open Access | Times Cited: 121
Deep learning neural networks for spatially explicit prediction of flash flood probability
Mahdi Panahi, Abolfazl Jaafari, Ataollah Shirzadi, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101076-101076
Open Access | Times Cited: 120
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed
Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, et al.
Forests (2019) Vol. 10, Iss. 9, pp. 743-743
Open Access | Times Cited: 118
Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
Dieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, et al.
Remote Sensing (2019) Vol. 11, Iss. 8, pp. 931-931
Open Access | Times Cited: 116
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Narges Kariminejad, et al.
Geoscience Frontiers (2020) Vol. 11, Iss. 6, pp. 2207-2219
Open Access | Times Cited: 115
Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
Forests (2020) Vol. 11, Iss. 4, pp. 421-421
Open Access | Times Cited: 115
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility
Alireza Arabameri, M Yamani, Biswajeet Pradhan, et al.
The Science of The Total Environment (2019) Vol. 688, pp. 903-916
Closed Access | Times Cited: 114
A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)
Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, et al.
Sensors (2019) Vol. 19, Iss. 11, pp. 2444-2444
Open Access | Times Cited: 110
A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping
Mohammadtaghi Avand, Saeid Janizadeh, Seyed Amir Naghibi, et al.
Water (2019) Vol. 11, Iss. 10, pp. 2076-2076
Open Access | Times Cited: 103
GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models
Viet‐Ha Nhu, Saeid Janizadeh, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 6, pp. 2039-2039
Open Access | Times Cited: 98
Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling
Alireza Arabameri, Biswajeet Pradhan, Luigi Lombardo
CATENA (2019) Vol. 183, pp. 104223-104223
Open Access | Times Cited: 93
Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India
Goutam Das, Kabita Lepcha
SN Applied Sciences (2019) Vol. 1, Iss. 11
Open Access | Times Cited: 81
Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms
Viet‐Ha Nhu, Ayub Mohammadi, Himan Shahabi, et al.
Forests (2020) Vol. 11, Iss. 8, pp. 830-830
Open Access | Times Cited: 78
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2833-2833
Open Access | Times Cited: 78
Page 1 - Next Page

Scroll to top