Dinh Kha Dang

629 total citations
32 papers, 440 citations indexed

About

Dinh Kha Dang is a scholar working on Global and Planetary Change, Water Science and Technology and Environmental Engineering. According to data from OpenAlex, Dinh Kha Dang has authored 32 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Global and Planetary Change, 17 papers in Water Science and Technology and 14 papers in Environmental Engineering. Recurrent topics in Dinh Kha Dang's work include Flood Risk Assessment and Management (23 papers), Hydrology and Watershed Management Studies (16 papers) and Hydrological Forecasting Using AI (9 papers). Dinh Kha Dang is often cited by papers focused on Flood Risk Assessment and Management (23 papers), Hydrology and Watershed Management Studies (16 papers) and Hydrological Forecasting Using AI (9 papers). Dinh Kha Dang collaborates with scholars based in Vietnam, Romania and Moldova. Dinh Kha Dang's co-authors include Huu Duy Nguyen, Quang‐Thanh Bui, Quoc‐Huy Nguyen, Pingping Luo, Thanh Ngo‐Duc, Han Xue, Daniel Nover, Geoffrey Schladow, Kaoru Takara and Alexandru-Ionuţ Petrişor and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Scientific Reports.

In The Last Decade

Dinh Kha Dang

30 papers receiving 424 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dinh Kha Dang Vietnam 11 311 199 111 104 37 32 440
Miyuru B. Gunathilake Sri Lanka 14 300 1.0× 242 1.2× 155 1.4× 101 1.0× 20 0.5× 41 475
Manoranjan Muthusamy United Kingdom 11 292 0.9× 233 1.2× 172 1.5× 95 0.9× 26 0.7× 16 473
P. E. Zope India 7 414 1.3× 183 0.9× 141 1.3× 106 1.0× 32 0.9× 8 479
Bazel Al-Shaibah China 10 376 1.2× 129 0.6× 125 1.1× 94 0.9× 37 1.0× 12 456
Byung Sik Kim South Korea 11 290 0.9× 215 1.1× 110 1.0× 113 1.1× 30 0.8× 61 430
Alaba Boluwade Canada 14 214 0.7× 163 0.8× 125 1.1× 170 1.6× 30 0.8× 34 435
Guo Yu United States 12 452 1.5× 282 1.4× 192 1.7× 164 1.6× 27 0.7× 24 576
Simon Moulds United Kingdom 13 271 0.9× 160 0.8× 122 1.1× 120 1.2× 27 0.7× 31 505
Sanjib Sharma United States 13 315 1.0× 239 1.2× 122 1.1× 155 1.5× 28 0.8× 40 465
Shiang‐Jen Wu Taiwan 11 290 0.9× 208 1.0× 121 1.1× 105 1.0× 21 0.6× 36 385

Countries citing papers authored by Dinh Kha Dang

Since Specialization
Citations

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

Fields of papers citing papers by Dinh Kha Dang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dinh Kha Dang. 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 Dinh Kha Dang. The network helps show where Dinh Kha Dang may publish in the future.

Co-authorship network of co-authors of Dinh Kha Dang

This figure shows the co-authorship network connecting the top 25 collaborators of Dinh Kha Dang. A scholar is included among the top collaborators of Dinh Kha Dang 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 Dinh Kha Dang. Dinh Kha Dang 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.
Dang, Dinh Kha, et al.. (2025). Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin. Journal of Hydroinformatics. 27(2). 275–298. 3 indexed citations
2.
Nguyen, Huu Duy, Dinh Kha Dang, & Quang‐Thanh Bui. (2025). Estuary salinity prediction using machine learning: case study in the Hau estuary in Mekong River, Vietnam. Water Science & Technology Water Supply. 25(2). 327–342. 2 indexed citations
3.
Nguyen, Huu Duy, Quoc‐Huy Nguyen, Dinh Kha Dang, et al.. (2024). Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam. Acta Geophysica. 72(6). 4395–4413. 8 indexed citations
4.
Dang, Dinh Kha, et al.. (2024). Flood risk assessment using machine learning, hydrodynamic modelling, and the analytic hierarchy process. Journal of Hydroinformatics. 26(8). 1852–1882. 9 indexed citations
5.
Nguyen, Huu Duy, Quoc‐Huy Nguyen, Dinh Kha Dang, et al.. (2024). A novel flood risk management approach based on future climate and land use change scenarios. The Science of The Total Environment. 921. 171204–171204. 30 indexed citations
6.
Nguyen, Huu Duy, Van Hong Nguyen, Dinh Kha Dang, et al.. (2024). Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam. Earth Science Informatics. 17(2). 1569–1589. 7 indexed citations
7.
Nguyen, Huu Duy, Dinh Kha Dang, Quoc‐Huy Nguyen, et al.. (2024). Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam. Environmental Research Letters. 19(10). 104033–104033. 5 indexed citations
8.
Dang, Dinh Kha, et al.. (2024). Flood hazard assessment using machine learning and hydrodynamic modeling: case study in the Vu Ga–Thu Bon basin in Vietnam. Water Practice & Technology. 19(10). 4104–4127.
9.
Nguyen, Huu Duy, et al.. (2023). Flood hazard and resilience in the watershed Nhat Le–Kien Giang in Vietnam. SHILAP Revista de lepidopterología. 2 indexed citations
10.
Nguyen, Huu Duy, et al.. (2023). Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation. Journal of Water and Climate Change. 15(1). 284–304. 17 indexed citations
11.
12.
Nguyen, Huu Duy, et al.. (2023). Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta. Environmental Science and Pollution Research. 30(29). 74340–74357. 13 indexed citations
13.
Nguyen, Huu Duy, Dinh Kha Dang, Quang‐Thanh Bui, & Alexandru-Ionuţ Petrişor. (2023). Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam. Transactions in GIS. 27(5). 1614–1640. 10 indexed citations
14.
Nguyen, Huu Duy, et al.. (2022). Assessment of upbasin dam impacts on streamflow at Chiang Saen gauging station during the period 1960–2020 in the context of statistical studies. River Research and Applications. 38(7). 1237–1253. 1 indexed citations
15.
Dang, Dinh Kha, et al.. (2022). A composite approach towards understanding the mechanisms and driving variables of river mouth variability: A case study of the Da Dien River mouth. Coastal Engineering Journal. 64(4). 533–550. 1 indexed citations
16.
Nguyen, Huu Duy, et al.. (2022). Flood susceptibility mapping using advanced hybrid machine learning and CyGNSS: a case study of Nghe An province, Vietnam. Acta Geophysica. 70(6). 2785–2803. 3 indexed citations
17.
Nguyen, Huu Duy, Dennis Fox, Dinh Kha Dang, et al.. (2021). Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam. Remote Sensing. 13(2). 262–262. 39 indexed citations
18.
19.
Dang, Dinh Kha, et al.. (2018). An Approach for Flow Forecasting in Ungauged Catchments – A Case Study for Ho Ho reservoir catchment, Ngan Sau River, Central Vietnam. Journal of Ecological Engineering. 19(3). 74–79. 5 indexed citations
20.
Luo, Pingping, Han Xue, Thanh Ngo‐Duc, et al.. (2018). Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Scientific Reports. 8(1). 12623–12623. 148 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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