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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:

Deep learning for geological hazards analysis: Data, models, applications, and opportunities
Zhengjing Ma, Gang Mei
Earth-Science Reviews (2021) Vol. 223, pp. 103858-103858
Open Access | Times Cited: 197

Showing 1-25 of 197 citing articles:

Future of machine learning in geotechnics
Kok‐Kwang Phoon, Wengang Zhang
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2022) Vol. 17, Iss. 1, pp. 7-22
Closed Access | Times Cited: 159
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities
Wei Han, Xiaohan Zhang, Yi Wang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 202, pp. 87-113
Closed Access | Times Cited: 114
A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction
Junwei Ma, Ding Xia, Yankun Wang, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 114, pp. 105150-105150
Open Access | Times Cited: 95
Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study
Junwei Ma, Ding Xia, Haixiang Guo, et al.
Landslides (2022) Vol. 19, Iss. 10, pp. 2489-2511
Open Access | Times Cited: 91
Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils
Lin Wang, Chongzhi Wu, Zhiyong Yang, et al.
Computers and Geotechnics (2023) Vol. 159, pp. 105413-105413
Closed Access | Times Cited: 71
Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory
Faming Huang, Haowen Xiong, Shui‐Hua Jiang, et al.
Earth-Science Reviews (2024) Vol. 250, pp. 104700-104700
Closed Access | Times Cited: 57
Iterative integration of deep learning in hybrid Earth surface system modelling
Min Chen, Zhen Qian, Niklas Boers, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 568-581
Closed Access | Times Cited: 56
A review of recent earthquake-induced landslides on the Tibetan Plateau
Bo Zhao, Lijun Su, Qiang Xu, et al.
Earth-Science Reviews (2023) Vol. 244, pp. 104534-104534
Closed Access | Times Cited: 44
Predictive deep learning for pitting corrosion modeling in buried transmission pipelines
Behnam Akhlaghi, Hassan Mesghali, Majid Ehteshami, et al.
Process Safety and Environmental Protection (2023) Vol. 174, pp. 320-327
Open Access | Times Cited: 43
A systematic review of trustworthy artificial intelligence applications in natural disasters
A. S. Albahri, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, et al.
Computers & Electrical Engineering (2024) Vol. 118, pp. 109409-109409
Open Access | Times Cited: 42
A review of remote sensing image segmentation by deep learning methods
Jiangyun Li, Yuanxiu Cai, Qing Li, et al.
International Journal of Digital Earth (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 20
Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties
Luqi Wang, Lin Wang, Wengang Zhang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 10, pp. 3951-3960
Open Access | Times Cited: 17
Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks
Yiqi Jiang, Chaolin Li, H.K. Song, et al.
Journal of Hazardous Materials (2022) Vol. 432, pp. 128732-128732
Closed Access | Times Cited: 46
A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Bahareh Ghasemian, Himan Shahabi, Ataollah Shirzadi, et al.
Sensors (2022) Vol. 22, Iss. 4, pp. 1573-1573
Open Access | Times Cited: 44
SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution
Jingzhi Tu, Gang Mei, Zhengjing Ma, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022) Vol. 15, pp. 5662-5673
Open Access | Times Cited: 44
Recent trends of machine learning applied to multi-source data of medicinal plants
Yanying Zhang, Yuanzhong Wang
Journal of Pharmaceutical Analysis (2023) Vol. 13, Iss. 12, pp. 1388-1407
Open Access | Times Cited: 39
A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations
Yanan Jiang, Huiyuan Luo, Qiang Xu, et al.
Remote Sensing (2022) Vol. 14, Iss. 4, pp. 1016-1016
Open Access | Times Cited: 38
Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model
Haoyuan Hong
Ecological Indicators (2023) Vol. 147, pp. 109968-109968
Open Access | Times Cited: 38
An Improved Faster R-CNN Method for Landslide Detection in Remote Sensing Images
Han Qin, Jizhou Wang, Xi Mao, et al.
Journal of Geovisualization and Spatial Analysis (2023) Vol. 8, Iss. 1
Closed Access | Times Cited: 34
Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models
Haiwei Yu, Wenjie Pei, Jingyi Zhang, et al.
Remote Sensing (2023) Vol. 15, Iss. 7, pp. 1886-1886
Open Access | Times Cited: 30
An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features
Yi He, Zhan’ao Zhao, Qing Zhu, et al.
International Journal of Digital Earth (2023) Vol. 17, Iss. 1
Open Access | Times Cited: 26
Comparison of natural breaks method and frequency ratio dividing attribute intervals for landslide susceptibility mapping
Chaoying Ke, Shu He, Yigen Qin
Bulletin of Engineering Geology and the Environment (2023) Vol. 82, Iss. 10
Closed Access | Times Cited: 24
TBM performance prediction using LSTM-based hybrid neural network model: Case study of Baimang River tunnel project in Shenzhen, China
Qihang Xu, Xin Huang, Baogang Zhang, et al.
Underground Space (2023) Vol. 11, pp. 130-152
Open Access | Times Cited: 23
An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples
Yongjun He, Jinfei Wang, Ying Zhang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 207, pp. 338-358
Closed Access | Times Cited: 12
Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images
Gong Cheng, Zixuan Wang, Cheng Huang, et al.
Remote Sensing (2024) Vol. 16, Iss. 10, pp. 1787-1787
Open Access | Times Cited: 11
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