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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 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

Showing 1-25 of 105 citing articles:

Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
Faming Huang, Zhongshan Cao, Jianfei Guo, et al.
CATENA (2020) Vol. 191, pp. 104580-104580
Closed Access | Times Cited: 384
Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
Zhilu Chang, Zhen Du, Fan Zhang, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 502-502
Open Access | Times Cited: 238
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
GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
Wei Chen, Yang Li
CATENA (2020) Vol. 195, pp. 104777-104777
Closed Access | Times Cited: 203
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
Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
Zhilu Chang, Filippo Catani, Faming Huang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 5, pp. 1127-1143
Open Access | Times Cited: 123
Landslide susceptibility mapping in Three Gorges Reservoir area based on GIS and boosting decision tree model
Fasheng Miao, Fancheng Zhao, Yiping Wu, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2283-2303
Open Access | Times Cited: 53
Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)
Yaming Tang, Fan Feng, Zizheng Guo, et al.
Journal of Cleaner Production (2020) Vol. 277, pp. 124159-124159
Closed Access | Times Cited: 111
Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models
Deying Li, Faming Huang, Liangxuan Yan, et al.
Applied Sciences (2019) Vol. 9, Iss. 18, pp. 3664-3664
Open Access | Times Cited: 109
Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
Li Zhu, Liang-Hao Huang, Linyu Fan, et al.
Sensors (2020) Vol. 20, Iss. 6, pp. 1576-1576
Open Access | Times Cited: 98
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
Refined landslide susceptibility analysis based on InSAR technology and UAV multi-source data
Chen Cao, Kuanxing Zhu, Peihua Xu, et al.
Journal of Cleaner Production (2022) Vol. 368, pp. 133146-133146
Closed Access | Times Cited: 46
Landslide identification using machine learning techniques: Review, motivation, and future prospects
S. Sreelakshmi, S. S. Vinod Chandra, E. Shaji
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2063-2090
Closed Access | Times Cited: 40
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
Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia
Arip Syaripudin Nur, Yong Je Kim, Joon Lee, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 760-760
Open Access | Times Cited: 29
Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps
Sheela Bhuvanendran Bhagya, Anita Saji Sumi, S. Balaji, et al.
Land (2023) Vol. 12, Iss. 2, pp. 468-468
Open Access | Times Cited: 28
GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach
Vincent E. Nwazelibe, Johnbosco C. Egbueri, Chinanu O. Unigwe, et al.
Environmental Earth Sciences (2023) Vol. 82, Iss. 19
Closed Access | Times Cited: 25
Refined landslide susceptibility mapping in township area using ensemble machine learning method under dataset replenishment strategy
Fancheng Zhao, Fasheng Miao, Yiping Wu, et al.
Gondwana Research (2024) Vol. 131, pp. 20-37
Closed Access | Times Cited: 11
Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway
Mohib Ullah, Haijun Qiu, Wenchao Huangfu, et al.
Land (2025) Vol. 14, Iss. 1, pp. 172-172
Open Access | Times Cited: 1
Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea
Sunmin Lee, Moung-Jin Lee, Hyung-Sup Jung, et al.
Geocarto International (2019) Vol. 35, Iss. 15, pp. 1665-1679
Closed Access | Times Cited: 72
Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea
Sunmin Lee, Yunjung Hyun, Moung-Jin Lee
Sustainability (2019) Vol. 11, Iss. 6, pp. 1678-1678
Open Access | Times Cited: 59
Spatial Landslide Risk Assessment at Phuentsholing, Bhutan
Abhirup Dikshit, Raju Sarkar, Biswajeet Pradhan, et al.
Geosciences (2020) Vol. 10, Iss. 4, pp. 131-131
Open Access | Times Cited: 53
Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: a Comparison of AHP and Frequency Ratio Methods
Anjana V. Thomas, Sunil Saha, Jean Homian Danumah, et al.
Journal of Geovisualization and Spatial Analysis (2021) Vol. 5, Iss. 2
Closed Access | Times Cited: 42
GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam
Huu Duy Nguyen
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2369-2386
Closed Access | Times Cited: 29
Machine learning toward improving the performance of membrane-based wastewater treatment: A review
Panchan Dansawad, Yanxiang Li, Yize Li, et al.
Advanced Membranes (2023) Vol. 3, pp. 100072-100072
Open Access | Times Cited: 18
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