Deliang Sun

2.6k total citations · 4 hit papers
35 papers, 1.9k citations indexed

About

Deliang Sun is a scholar working on Management, Monitoring, Policy and Law, Atmospheric Science and Global and Planetary Change. According to data from OpenAlex, Deliang Sun has authored 35 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Management, Monitoring, Policy and Law, 15 papers in Atmospheric Science and 15 papers in Global and Planetary Change. Recurrent topics in Deliang Sun's work include Landslides and related hazards (24 papers), Cryospheric studies and observations (13 papers) and Geotechnical Engineering and Analysis (10 papers). Deliang Sun is often cited by papers focused on Landslides and related hazards (24 papers), Cryospheric studies and observations (13 papers) and Geotechnical Engineering and Analysis (10 papers). Deliang Sun collaborates with scholars based in China, United States and United Arab Emirates. Deliang Sun's co-authors include Haijia Wen, Jiahui Xu, Xinzhi Zhou, Jialan Zhang, Xianglong Ma, Junyi Zhang, Qingyu Gu, Fengtai Zhang, Jianping Wu and Yue Wang and has published in prestigious journals such as Journal of Cleaner Production, Journal of Environmental Management and International Journal of Environmental Research and Public Health.

In The Last Decade

Deliang Sun

32 papers receiving 1.8k citations

Hit Papers

A random forest model of landslide susceptibility mapping... 2020 2026 2022 2024 2020 2023 2020 2024 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deliang Sun China 17 1.2k 808 461 413 321 35 1.9k
Emrehan Kutluğ Şahin Türkiye 17 1.3k 1.1× 960 1.2× 389 0.8× 416 1.0× 370 1.2× 29 2.1k
Ling Peng China 15 1.2k 1.0× 958 1.2× 454 1.0× 291 0.7× 282 0.9× 20 1.7k
Abdelaziz Merghadi Algeria 9 1.8k 1.4× 1.2k 1.5× 614 1.3× 439 1.1× 428 1.3× 12 2.3k
Jianbing Peng China 25 1.4k 1.1× 604 0.7× 459 1.0× 444 1.1× 289 0.9× 97 2.1k
İsmail Çölkesen Türkiye 18 1.2k 0.9× 1.1k 1.3× 573 1.2× 273 0.7× 334 1.0× 45 2.3k
Sansar Raj Meena Italy 22 1.7k 1.4× 1.1k 1.4× 581 1.3× 270 0.7× 236 0.7× 50 2.3k
Zhongfan Zhu China 17 1.3k 1.0× 1.1k 1.3× 491 1.1× 281 0.7× 308 1.0× 66 2.2k
Trần Anh Tuấn Vietnam 9 889 0.7× 713 0.9× 271 0.6× 213 0.5× 224 0.7× 38 1.4k
Haijia Wen China 25 2.0k 1.6× 1.2k 1.5× 732 1.6× 644 1.6× 460 1.4× 82 2.9k
Omar F. Althuwaynee Malaysia 19 1.4k 1.2× 1.3k 1.7× 452 1.0× 336 0.8× 343 1.1× 33 2.1k

Countries citing papers authored by Deliang Sun

Since Specialization
Citations

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

Fields of papers citing papers by Deliang Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deliang Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Deliang Sun. A scholar is included among the top collaborators of Deliang Sun 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 Deliang Sun. Deliang Sun 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
2.
Zhang, Qiang, R. Iestyn Woolway, Tingxi Liu, et al.. (2025). Global Warming Will Increase the Risk of Water Shortage in Northwest China. Earth s Future. 13(5). 6 indexed citations
4.
Sun, Deliang, et al.. (2024). Improving generalization performance of landslide susceptibility model considering spatial heterogeneity by using the geomorphic label-based LightGBM. Bulletin of Engineering Geology and the Environment. 83(9). 3 indexed citations
5.
Sun, Deliang, et al.. (2024). Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China. Journal of Rock Mechanics and Geotechnical Engineering. 16(8). 3221–3232. 15 indexed citations
6.
Sun, Deliang, Xiaoqing Wu, Haijia Wen, et al.. (2024). Ecological Security Pattern based on XGBoost-MCR model: A case study of the Three Gorges Reservoir Region. Journal of Cleaner Production. 470. 143252–143252. 46 indexed citations breakdown →
7.
Sun, Deliang, Haijia Wen, Fengtai Zhang, et al.. (2024). SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China. The Egyptian Journal of Remote Sensing and Space Science. 27(3). 508–523. 13 indexed citations
8.
Sun, Deliang, et al.. (2023). A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples. Geomatics Natural Hazards and Risk. 14(1). 64 indexed citations
11.
Sun, Deliang, et al.. (2023). A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping. Earth Surface Processes and Landforms. 49(1). 304–317. 4 indexed citations
12.
Sun, Deliang, Jing Wang, Haijia Wen, et al.. (2023). Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning. Earth Surface Processes and Landforms. 49(3). 1006–1027. 5 indexed citations
13.
Zhang, Junyi, Xianglong Ma, Jialan Zhang, et al.. (2023). Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. Journal of Environmental Management. 332. 117357–117357. 322 indexed citations breakdown →
14.
Sun, Deliang, Jialan Zhang, Haijia Wen, et al.. (2022). Essential insights into decision mechanism of landslide susceptibility mapping based on different machine learning models. Geocarto International. 38(1). 1–29. 22 indexed citations
15.
Sun, Deliang, et al.. (2022). A Hybrid Landslide Warning Model Coupling Susceptibility Zoning and Precipitation. Forests. 13(6). 827–827. 43 indexed citations
16.
Wang, Yue, Haijia Wen, Deliang Sun, & Yuechen Li. (2021). Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector. Remote Sensing. 13(13). 2625–2625. 59 indexed citations
17.
Sun, Deliang, Jiahui Xu, Haijia Wen, & Yue Wang. (2020). An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science. 31(6). 1068–1086. 95 indexed citations
18.
Wang, Yue, Deliang Sun, Haijia Wen, Hong Zhang, & Fengtai Zhang. (2020). Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China). International Journal of Environmental Research and Public Health. 17(12). 4206–4206. 106 indexed citations
19.
Sun, Deliang, Behnam Askarian, Danial Jahed Armaghani, et al.. (2020). Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index. Applied Sciences. 10(5). 1691–1691. 41 indexed citations
20.
Sun, Deliang, et al.. (2020). An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide. Natural Hazards. 105(2). 1255–1279. 37 indexed citations

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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