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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:

A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison
Omar F. Althuwaynee, Biswajeet Pradhan, Saro Lee
International Journal of Remote Sensing (2016) Vol. 37, Iss. 5, pp. 1190-1209
Open Access | Times Cited: 116

Showing 1-25 of 116 citing articles:

Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
Wei Chen, Hamid Reza Pourghasemi, Aiding Kornejady, et al.
Geoderma (2017) Vol. 305, pp. 314-327
Closed Access | Times Cited: 362
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
Yanli Wu, Yutian Ke, Zhuo Chen, et al.
CATENA (2019) Vol. 187, pp. 104396-104396
Open Access | Times Cited: 346
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
Ahmed M. Youssef, Hamid Reza Pourghasemi
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 639-655
Open Access | Times Cited: 336
Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques
Mahyat Shafapour Tehrany, Simon Jones, Farzin Shabani
CATENA (2018) Vol. 175, pp. 174-192
Closed Access | Times Cited: 297
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Zhice Fang, Yi Wang, Ling Peng, et al.
Computers & Geosciences (2020) Vol. 139, pp. 104470-104470
Closed Access | Times Cited: 267
Shallow landslide susceptibility assessment using a novel hybrid intelligence approach
Ataollah Shirzadi, Dieu Tien Bui, Binh Thai Pham, et al.
Environmental Earth Sciences (2017) Vol. 76, Iss. 2
Closed Access | Times Cited: 250
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
Wei Chen, Mahdi Panahi, Paraskevas Tsangaratos, et al.
CATENA (2018) Vol. 172, pp. 212-231
Closed Access | Times Cited: 241
GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models
Wei Chen, Xiaoshen Xie, Jianbing Peng, et al.
Geomatics Natural Hazards and Risk (2017) Vol. 8, Iss. 2, pp. 950-973
Open Access | Times Cited: 203
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
Sk Ajim Ali, Farhana Parvin, Jana Vojteková, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 857-876
Open Access | Times Cited: 200
Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia
Umar Lawal Dano, Abdul‐Lateef Balogun, Abdul-Nasir Matori, et al.
Water (2019) Vol. 11, Iss. 3, pp. 615-615
Open Access | Times Cited: 186
Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
Ataollah Shirzadi, Karim Soliamani, Mahmood Habibnejhad, et al.
Sensors (2018) Vol. 18, Iss. 11, pp. 3777-3777
Open Access | Times Cited: 180
Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)
Wei Chen, Xusheng Yan, Zhou Zhao, et al.
Bulletin of Engineering Geology and the Environment (2018) Vol. 78, Iss. 1, pp. 247-266
Closed Access | Times Cited: 167
Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms
Abdul‐Lateef Balogun, Fatemeh Rezaie, Quoc Bao Pham, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101104-101104
Open Access | Times Cited: 142
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea
Wahyu Luqmanul Hakim, Fatemeh Rezaie, Arip Syaripudin Nur, et al.
Journal of Environmental Management (2021) Vol. 305, pp. 114367-114367
Closed Access | Times Cited: 139
Analytical hierarchy process for sustainable agriculture: An overview
Anuj Kumar, Sangeeta Pant
MethodsX (2022) Vol. 10, pp. 101954-101954
Open Access | Times Cited: 86
Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study
Taşkın Kavzoǧlu, İsmail Çölkesen, Emrehan Kutluğ Şahin
Advances in natural and technological hazards research (2018), pp. 283-301
Closed Access | Times Cited: 152
Performance analysis of classification algorithms on early detection of liver disease
Moloud Abdar, Mariam Zomorodi‐Moghadam, Resul Daş, et al.
Expert Systems with Applications (2016) Vol. 67, pp. 239-251
Closed Access | Times Cited: 140
A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment
Mousa Abedini, Bahareh Ghasemian, Ataollah Shirzadi, et al.
Geocarto International (2018) Vol. 34, Iss. 13, pp. 1427-1457
Closed Access | Times Cited: 130
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model
Javad Hassannataj Joloudari, Edris Hassannataj Joloudari, Hamid Saadatfar, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 3, pp. 731-731
Open Access | Times Cited: 127
Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models
Romulus Costache
The Science of The Total Environment (2018) Vol. 659, pp. 1115-1134
Closed Access | Times Cited: 116
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea
Sungjae Park, Chang-Wook Lee, Saro Lee, et al.
Remote Sensing (2018) Vol. 10, Iss. 10, pp. 1545-1545
Open Access | Times Cited: 105
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
Xuan Luan Truong, Muneki Mitamura, Yasuyuki Kono, et al.
Applied Sciences (2018) Vol. 8, Iss. 7, pp. 1046-1046
Open Access | Times Cited: 100
Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India
Indrajit Chowdhuri, Subodh Chandra Pal, Alireza Arabameri, et al.
Environmental Earth Sciences (2020) Vol. 79, Iss. 20
Closed Access | Times Cited: 86
Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran
Yousef Bahrami, Hossein Hassani, Abbas Maghsoudi
GeoJournal (2020) Vol. 86, Iss. 4, pp. 1797-1816
Closed Access | Times Cited: 83
Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
Maryam Sadat Jaafarzadeh, Naser Tahmasebipour, Ali Haghizadeh, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 80
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