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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:

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

Showing 1-25 of 152 citing articles:

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
Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
Binh Thai Pham, Indra Prakash, Sushant K. Singh, et al.
CATENA (2018) Vol. 175, pp. 203-218
Closed Access | Times Cited: 279
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Emrehan Kutluğ Şahin
SN Applied Sciences (2020) Vol. 2, Iss. 7
Open Access | Times Cited: 258
Landslide susceptibility evaluation and hazard zonation techniques – a review
Leulalem Shano, Tarun Kumar Raghuvanshi, Matebie Meten
Geoenvironmental Disasters (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 243
Review on remote sensing methods for landslide detection using machine and deep learning
Amrita Mohan, Amit Kumar Singh, Basant Kumar, et al.
Transactions on Emerging Telecommunications Technologies (2020) Vol. 32, Iss. 7
Closed Access | Times Cited: 237
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
Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Taşkın Kavzoǧlu, Alihan Teke
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 6, pp. 7367-7385
Closed Access | Times Cited: 191
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
Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks
Husam A. H. Al-Najjar, Biswajeet Pradhan
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 625-637
Open Access | Times Cited: 139
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
A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
Songlin Liu, Luqi Wang, Wengang Zhang, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2283-2301
Closed Access | Times Cited: 109
Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India)
Soumik Saha, Paromita Majumdar, Biswajit Bera
Quaternary Science Advances (2023) Vol. 10, pp. 100075-100075
Open Access | Times Cited: 44
Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
Viet‐Ha Nhu, Nhat‐Duc Hoang, Hieu Nguyen, et al.
CATENA (2020) Vol. 188, pp. 104458-104458
Closed Access | Times Cited: 138
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
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
PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches
Omid Rahmati, Aiding Kornejady, Mahmood Samadi, et al.
The Science of The Total Environment (2019) Vol. 664, pp. 296-311
Open Access | Times Cited: 97
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: 80
Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping
Emrehan Kutluğ Şahin, İsmail Çölkesen
Geocarto International (2019) Vol. 36, Iss. 11, pp. 1253-1275
Closed Access | Times Cited: 77
Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics
Romulus Costache, Haoyuan Hong, Yi Wang
CATENA (2019) Vol. 183, pp. 104179-104179
Closed Access | Times Cited: 77
A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility
Viet-Hung Dang, Nhat‐Duc Hoang, Le-Mai-Duyen Nguyen, et al.
Forests (2020) Vol. 11, Iss. 1, pp. 118-118
Open Access | Times Cited: 76
Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms
Alireza Arabameri, Subodh Chandra Pal, Romulus Costache, et al.
Geomatics Natural Hazards and Risk (2021) Vol. 12, Iss. 1, pp. 469-498
Open Access | Times Cited: 76
Using machine learning algorithms to map the groundwater recharge potential zones
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Saleh Yousefi, et al.
Journal of Environmental Management (2020) Vol. 265, pp. 110525-110525
Closed Access | Times Cited: 74
Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions
Haojie Cai, Tao Chen, Ruiqing Niu, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021) Vol. 14, pp. 5235-5247
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: 69
A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
Pengxiang Zhao, Zohreh Masoumi, Maryam Kalantari, et al.
Remote Sensing (2022) Vol. 14, Iss. 1, pp. 211-211
Open Access | Times Cited: 54
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