Zhice Fang

2.8k total citations · 4 hit papers
34 papers, 2.1k citations indexed

About

Zhice Fang is a scholar working on Management, Monitoring, Policy and Law, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Zhice Fang has authored 34 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Management, Monitoring, Policy and Law, 21 papers in Global and Planetary Change and 16 papers in Atmospheric Science. Recurrent topics in Zhice Fang's work include Landslides and related hazards (30 papers), Flood Risk Assessment and Management (19 papers) and Cryospheric studies and observations (12 papers). Zhice Fang is often cited by papers focused on Landslides and related hazards (30 papers), Flood Risk Assessment and Management (19 papers) and Cryospheric studies and observations (12 papers). Zhice Fang collaborates with scholars based in China, Netherlands and Austria. Zhice Fang's co-authors include Yi Wang, Haoyuan Hong, Ling Peng, Ling Peng, Mao Wang, Luigi Lombardo, Xianzhe Tang, Kashif Ullah, Romulus Costache and Lizhe Wang and has published in prestigious journals such as The Science of The Total Environment, IEEE Transactions on Geoscience and Remote Sensing and Journal of Hydrology.

In The Last Decade

Zhice Fang

31 papers receiving 2.1k citations

Hit Papers

Comparison of convolutional neural networks for landslide... 2019 2026 2021 2023 2019 2020 2019 2020 100 200 300 400
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.

  1. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
    2019 409 citations Yi Wang, Zhice Fang et al. The Science of The Total Environment profile →
  2. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
    2020 257 citations Zhice Fang, Yi Wang et al. profile →
  3. Flood susceptibility mapping using convolutional neural network frameworks
    2019 241 citations Yi Wang, Zhice Fang et al. profile →
  4. Comparative study of landslide susceptibility mapping with different recurrent neural networks
    2020 214 citations Yi Wang, Zhice Fang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhice Fang China 18 1.4k 1.3k 596 339 339 34 2.1k
Zhongfan Zhu China 17 1.1k 0.8× 1.3k 1.0× 491 0.8× 259 0.8× 323 1.0× 66 2.2k
Abhirup Dikshit Australia 26 1.3k 0.9× 878 0.7× 439 0.7× 376 1.1× 301 0.9× 38 2.0k
Ioanna Ilia Greece 20 1.5k 1.1× 1.5k 1.1× 447 0.8× 473 1.4× 410 1.2× 44 2.5k
Xiaoshen Xie China 10 1.2k 0.9× 1.1k 0.9× 331 0.6× 420 1.2× 369 1.1× 19 2.0k
M. B. Dholakia India 13 1.3k 0.9× 1.4k 1.1× 404 0.7× 351 1.0× 299 0.9× 19 2.1k
Omar F. Althuwaynee Malaysia 19 1.3k 1.0× 1.4k 1.1× 452 0.8× 331 1.0× 188 0.6× 33 2.1k
Ataollah Shirzadi Iran 22 1.5k 1.1× 1.1k 0.8× 386 0.6× 520 1.5× 404 1.2× 31 2.3k
Paraskevas Tsangaratos Greece 20 1.7k 1.3× 1.6k 1.2× 519 0.9× 572 1.7× 514 1.5× 62 2.9k
Ling Peng China 15 958 0.7× 1.2k 0.9× 454 0.8× 187 0.6× 151 0.4× 20 1.7k
İsmail Çölkesen Türkiye 18 1.1k 0.8× 1.2k 0.9× 573 1.0× 333 1.0× 101 0.3× 45 2.3k

Countries citing papers authored by Zhice Fang

Since Specialization
Citations

This map shows the geographic impact of Zhice Fang'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 Zhice Fang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhice Fang more than expected).

Fields of papers citing papers by Zhice Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhice Fang. 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 Zhice Fang. The network helps show where Zhice Fang may publish in the future.

Co-authorship network of co-authors of Zhice Fang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhice Fang. A scholar is included among the top collaborators of Zhice Fang 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 Zhice Fang. Zhice Fang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fang, Zhice, et al.. (2025). Climate change has increased rainfall-induced landslide damages in central China. International Journal of Disaster Risk Reduction. 119. 105320–105320. 5 indexed citations
2.
Wu, Xueling, et al.. (2025). Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment. Geo-spatial Information Science. 28(5). 2486–2506. 2 indexed citations
3.
Wang, Yi, et al.. (2025). Assessing the impact of precipitation variability on landslide hazards in urbanized regions. International Journal of Applied Earth Observation and Geoinformation. 136. 104360–104360. 2 indexed citations
4.
Wang, Tengfei, Kunlong Yin, Zheng Wang, et al.. (2025). Long and short-term perspectives on space–time landslide modelling. International Journal of Applied Earth Observation and Geoinformation. 142. 104694–104694.
5.
6.
Ullah, Kashif, Yi Wang, Penglei Li, et al.. (2024). Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios. Environmental Research Letters. 19(12). 124016–124016. 14 indexed citations
7.
Li, Chen, Yi Wang, Zhice Fang, & Penglei Li. (2024). Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–18. 3 indexed citations
8.
Li, Penglei, et al.. (2024). Unsupervised Landslide Detection From Multitemporal High-Resolution Images Based on Progressive Label Upgradation and Cross-Temporal Style Adaption. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15. 2 indexed citations
9.
Wang, Yi, et al.. (2024). Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change. Journal of Environmental Management. 373. 123474–123474. 4 indexed citations
10.
Zhang, Ruiqi, Lele Zhang, Zhice Fang, et al.. (2024). Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China. Remote Sensing. 16(13). 2394–2394. 15 indexed citations
11.
Fang, Zhice, Yi Wang, C.J. van Westen, & Luigi Lombardo. (2024). Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors. CATENA. 240. 107989–107989. 16 indexed citations
12.
Ma, Hao, et al.. (2024). An Integrated Framework of Positive-Unlabeled and Imbalanced Learning for Landslide Susceptibility Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 15596–15611. 2 indexed citations
13.
Fang, Zhice, Yi Wang, C.J. van Westen, & Luigi Lombardo. (2023). Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods. Mathematical Geosciences. 56(6). 1335–1354. 26 indexed citations
14.
Fang, Zhice, et al.. (2023). Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework. Geocarto International. 38(1). 12 indexed citations
15.
He, Kun, Luigi Lombardo, Ling Chang, et al.. (2023). Investigating earthquake legacy effect on hillslope deformation using InSAR‐derived time series. Earth Surface Processes and Landforms. 49(3). 980–990. 12 indexed citations
16.
Fang, Zhice, et al.. (2022). Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction. Stochastic Environmental Research and Risk Assessment. 36(10). 3535–3556. 22 indexed citations
17.
Fang, Zhice, Yi Wang, Ruiqing Niu, & Ling Peng. (2021). Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14. 11581–11592. 24 indexed citations
18.
Song, Jiahui, Yi Wang, Zhice Fang, Ling Peng, & Haoyuan Hong. (2020). Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13. 4642–4662. 48 indexed citations
19.
Fang, Zhice, Yi Wang, Ling Peng, & Haoyuan Hong. (2020). A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. International Journal of Geographical Information Systems. 35(2). 321–347. 197 indexed citations
20.
Wang, Yi, Zhice Fang, & Haoyuan Hong. (2019). Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. The Science of The Total Environment. 666. 975–993. 409 indexed citations breakdown →

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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