Ling Peng

2.1k total citations · 2 hit papers
20 papers, 1.7k citations indexed

About

Ling Peng is a scholar working on Management, Monitoring, Policy and Law, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Ling Peng has authored 20 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Management, Monitoring, Policy and Law, 12 papers in Global and Planetary Change and 9 papers in Atmospheric Science. Recurrent topics in Ling Peng's work include Landslides and related hazards (16 papers), Flood Risk Assessment and Management (11 papers) and Cryospheric studies and observations (7 papers). Ling Peng is often cited by papers focused on Landslides and related hazards (16 papers), Flood Risk Assessment and Management (11 papers) and Cryospheric studies and observations (7 papers). Ling Peng collaborates with scholars based in China, Austria and Australia. Ling Peng's co-authors include Yi Wang, Zhice Fang, Haoyuan Hong, Ruiqing Niu, Xueling Wu, Runqing Ye, Mao Wang, Yannan Zhao, Bo Huang and Tao Chen and has published in prestigious journals such as Journal of Hydrology, Remote Sensing and Geomorphology.

In The Last Decade

Ling Peng

20 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

2020 257 citations Zhice Fang, Yi Wang et al. Computers & Geosciences profile →
Comparative study of landslide susceptibility mapping with different recurrent neural networks

2020 214 citations Yi Wang, Zhice Fang et al. Computers & Geosciences profile →

Peers

Countries citing papers authored by Ling Peng

Since Specialization

Citations

This map shows the geographic impact of Ling Peng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ling Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling Peng more than expected).

Fields of papers citing papers by Ling Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ling Peng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ling Peng. The network helps show where Ling Peng may publish in the future.

Co-authorship network of co-authors of Ling Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Peng. A scholar is included among the top collaborators of Ling Peng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ling Peng. Ling Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	TwoStream-EQT: A microseismic phase picking model combining time and frequency domain inputs 2025 ·Computers & Geosciences ·Ling Peng, Lei Li, S. Mostafa Mousavi, (unknown), Gregory C. Beroza	1
2	Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation 2024 ·Applied Sciences ·Lei Li, (unknown), Xinpeng Pan, Ling Peng, Yuyang Tan, Jianxin Liu	4
3	Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction 2022 ·Stochastic Environmental Research and Risk Assessment ·Zhice Fang, Yi Wang, (unknown), Ruiqing Niu, Ling Peng	22
4	Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling 2021 ·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ·Zhice Fang, Yi Wang, Ruiqing Niu, Ling Peng	24
5	Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China 2021 ·Remote Sensing ·Zhice Fang, Yi Wang, (unknown), Ling Peng	43
6	A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping 2020 ·International Journal of Geographical Information Systems ·Zhice Fang, Yi Wang, Ling Peng, Haoyuan Hong	197
7	Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models 2020 ·Journal of Mountain Science ·Tao Chen, (unknown), Ruiqing Niu, John Trinder, Ling Peng, Tao Lei	104
8	Comparative study of landslide susceptibility mapping with different recurrent neural networks breakdown → 2020 ·Computers & Geosciences ·Yi Wang, Zhice Fang, Mao Wang, Ling Peng, Haoyuan Hong	214
9	Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping breakdown → 2020 ·Computers & Geosciences ·Zhice Fang, Yi Wang, Ling Peng, Haoyuan Hong	257
10	Predicting flood susceptibility using LSTM neural networks 2020 ·Journal of Hydrology ·Zhice Fang, Yi Wang, Ling Peng, Haoyuan Hong	192
11	Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China) 2018 ·ISPRS International Journal of Geo-Information ·Yingxu Song, Ruiqing Niu, Shiluo Xu, Runqing Ye, Ling Peng, Tao Guo, Shiyao Li, Tao Chen	86
12	Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China 2017 ·Remote Sensing ·Qian Wang, Yi Wang, Ruiqing Niu, Ling Peng	85
13	Classification of GF-1 Remote Sensing Image Based on Random Forests for Urban Land-use 2016 ·Bulletin of Surveying and Mapping ·(unknown), Tianhe Chi, Ling Peng, (unknown), (unknown)	7
14	Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China 2016 ·Environmental Earth Sciences ·Miao Zhang, (unknown), Ling Peng, Ruiqing Niu	26
15	A rapid extraction of landslide disaster information research based on GF-1 image 2015 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·(unknown), (unknown), Ling Peng, (unknown), Na Wang	2
16	Quantitative risk analysis for landslides: the case of the Three Gorges area, China 2014 ·Landslides ·Ling Peng, Suning Xu, (unknown), Junhuan Peng	68
17	Susceptibility Assessment of Landslides Triggered by the Lushan Earthquake, April 20, 2013, China 2014 ·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ·Ruiqing Niu, Xueling Wu, (unknown), Ling Peng, Li Ai, Junhuan Peng	33
18	Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China 2013 ·Environmental Earth Sciences ·Xueling Wu, Ruiqing Niu, Fu Ren, Ling Peng	54
19	Prediction of Landslide Displacement Based on Kernel Principal Component Analysis and Neural Network-Markov Chain 2013 ·Advanced materials research ·Yan Zhao, (unknown), Jiong Li, Ling Peng, Yi Wang	6
20	Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China 2013 ·Geomorphology ·Ling Peng, Ruiqing Niu, Bo Huang, Xueling Wu, Yannan Zhao, Runqing Ye	230

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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