Tran Van Phong

4.1k total citations · 2 hit papers
61 papers, 3.2k citations indexed

About

Tran Van Phong is a scholar working on Global and Planetary Change, Management, Monitoring, Policy and Law and Environmental Engineering. According to data from OpenAlex, Tran Van Phong has authored 61 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Global and Planetary Change, 28 papers in Management, Monitoring, Policy and Law and 17 papers in Environmental Engineering. Recurrent topics in Tran Van Phong's work include Flood Risk Assessment and Management (43 papers), Landslides and related hazards (28 papers) and Hydrology and Watershed Management Studies (17 papers). Tran Van Phong is often cited by papers focused on Flood Risk Assessment and Management (43 papers), Landslides and related hazards (28 papers) and Hydrology and Watershed Management Studies (17 papers). Tran Van Phong collaborates with scholars based in Vietnam, India and Iran. Tran Van Phong's co-authors include Binh Thai Pham, Indra Prakash, Hiep Van Le, Huu Duy Nguyen, Abolfazl Jaafari, Phan Trọng Trịnh, Nadhir Al‐Ansari, Chinh Luu, Mohammadtaghi Avand and Hai‐Bang Ly and has published in prestigious journals such as Journal of Hydrology, International Journal of Environmental Research and Public Health and Sustainability.

In The Last Decade

Tran Van Phong

59 papers receiving 3.1k 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.

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

2020 264 citations Dong Van Dao, Mahmoud Bayat et al. CATENA profile →
Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

2020 192 citations Binh Thai Pham, Abolfazl Jaafari et al. Symmetry profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tran Van Phong Vietnam	31	2.2k	1.2k	970	869	480	61	3.2k
Fatemeh Rezaie South Korea	29	1.7k 0.8×	1.1k 0.9×	1.0k 1.1×	775 0.9×	459 1.0×	64	3.2k
Saied Pirasteh China	33	1.5k 0.7×	1.1k 0.9×	1.0k 1.1×	616 0.7×	508 1.1×	147	3.4k
Sunil Saha India	37	2.3k 1.0×	1.6k 1.3×	1.1k 1.2×	671 0.8×	458 1.0×	90	3.7k
Hiep Van Le Vietnam	32	1.7k 0.8×	784 0.6×	854 0.9×	769 0.9×	330 0.7×	65	3.3k
Bahareh Kalantar Japan	30	1.5k 0.7×	894 0.7×	1.1k 1.2×	555 0.6×	331 0.7×	76	3.2k
Paraskevas Tsangaratos Greece	20	1.7k 0.8×	1.6k 1.3×	572 0.6×	514 0.6×	519 1.1×	62	2.9k
Ataollah Shirzadi Iran	22	1.5k 0.7×	1.1k 0.9×	520 0.5×	404 0.5×	386 0.8×	31	2.3k
Mustafa Neamah Jebur Malaysia	24	3.2k 1.4×	1.4k 1.1×	1.3k 1.3×	1.5k 1.7×	707 1.5×	26	4.1k
Ioanna Ilia Greece	20	1.5k 0.7×	1.5k 1.2×	473 0.5×	410 0.5×	447 0.9×	44	2.5k
Seyed Amir Naghibi Iran	30	2.7k 1.2×	1.1k 0.9×	2.5k 2.6×	1.7k 2.0×	372 0.8×	48	4.6k

Countries citing papers authored by Tran Van Phong

Since Specialization

Citations

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

Fields of papers citing papers by Tran Van Phong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tran Van Phong. 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 Tran Van Phong. The network helps show where Tran Van Phong may publish in the future.

Co-authorship network of co-authors of Tran Van Phong

This figure shows the co-authorship network connecting the top 25 collaborators of Tran Van Phong. A scholar is included among the top collaborators of Tran Van Phong 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 Tran Van Phong. Tran Van Phong 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

Nguyen, Manh Duc, Nguyen Tuan Hung, Cong Cao, et al.. (2024). Enhanced Landslide Spatial Prediction Using Hybrid Deep Learning Model and SHAP Analysis: A Case Study of the Tuyen Quang-Ha Giang Expressway, Vietnam. Journal of the Indian Society of Remote Sensing. 53(5). 1647–1666. 1 indexed citations

Wang, Zhengtao, et al.. (2024). Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping. Earth and Space Science. 11(6). 2 indexed citations

Hiếu, Nguyễn Minh, Tran Van Phong, Mahdis Amiri, et al.. (2021). Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin. Geomatics Natural Hazards and Risk. 12(1). 1688–1714. 13 indexed citations

Pham, Binh Thai, Vu Duy Vinh, Romulus Costache, et al.. (2021). Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto International. 37(18). 5175–5200. 39 indexed citations

Dao, Dong Van, Mahmoud Bayat, Davood Mafi-Gholami, et al.. (2020). A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA. 188. 104451–104451. 264 indexed citations breakdown →

Pham, Binh Thai, Tran Van Phong, Mohammadtaghi Avand, et al.. (2020). Improving Voting Feature Intervals for Spatial Prediction of Landslides. Mathematical Problems in Engineering. 2020. 1–15. 12 indexed citations

Pham, Binh Thai, Chinh Luu, Tran Van Phong, et al.. (2020). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology. 592. 125815–125815. 147 indexed citations

Pham, Binh Thai, T. Nguyen‐Thoi, Chongchong Qi, et al.. (2020). Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA. 195. 104805–104805. 125 indexed citations

Pham, Binh Thai, Tran Van Phong, T. Nguyen‐Thoi, et al.. (2020). Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto International. 37(3). 735–757. 76 indexed citations

10.

Pham, Binh Thai, Mohammadtaghi Avand, Saeid Janizadeh, et al.. (2020). GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water. 12(3). 683–683. 186 indexed citations

11.

Pham, Binh Thai, Abolfazl Jaafari, Mohammadtaghi Avand, et al.. (2020). Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry. 12(6). 1022–1022. 192 indexed citations breakdown →

12.

Pham, Binh Thai, Tran Van Phong, Huu Duy Nguyen, et al.. (2020). A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water. 12(1). 239–239. 109 indexed citations

13.

Pham, Binh Thai, Chinh Luu, Tran Van Phong, et al.. (2020). Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?. Journal of Hydrology. 592. 125615–125615. 106 indexed citations

14.

Ha, Duong Hai, Mohammadtaghi Avand, Abolfazl Jaafari, et al.. (2020). Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences. 10(7). 2469–2469. 121 indexed citations

15.

Ha, Duong Hai, Huu Duy Nguyen, Tran Van Phong, et al.. (2020). Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability. 12(7). 2622–2622. 47 indexed citations

16.

Liêm, Ngô Văn, Nadhir Al‐Ansari, Tran Van Phong, et al.. (2019). GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability. 11(24). 7118–7118. 45 indexed citations

17.

Tuyen, Tran Thi, Ataollah Shirzadi, Binh Thai Pham, et al.. (2019). Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction. Applied Sciences. 9(14). 2824–2824. 57 indexed citations

18.

Janizadeh, Saeid, Mohammadtaghi Avand, Abolfazl Jaafari, et al.. (2019). Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability. 11(19). 5426–5426. 216 indexed citations

19.

Phong, Tran Van, Phan Trọng Trịnh, Indra Prakash, et al.. (2019). Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto International. 36(15). 1685–1708. 77 indexed citations

20.

Pham, Binh Thai, Indra Prakash, Jie Dou, et al.. (2018). A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto International. 35(12). 1267–1292. 149 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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