M.T. Vu

436 total citations
19 papers, 322 citations indexed

About

M.T. Vu is a scholar working on Environmental Engineering, Ocean Engineering and Geophysics. According to data from OpenAlex, M.T. Vu has authored 19 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Environmental Engineering, 7 papers in Ocean Engineering and 7 papers in Geophysics. Recurrent topics in M.T. Vu's work include Groundwater flow and contamination studies (10 papers), Geophysical and Geoelectrical Methods (5 papers) and Hydrological Forecasting Using AI (4 papers). M.T. Vu is often cited by papers focused on Groundwater flow and contamination studies (10 papers), Geophysical and Geoelectrical Methods (5 papers) and Hydrological Forecasting Using AI (4 papers). M.T. Vu collaborates with scholars based in France, Vietnam and United States. M.T. Vu's co-authors include Abderrahim Jardani, Nicolas Masséi, Matthieu Fournier, P. M. Adler, S. Békri, P. M. Adler, M. Krimissa, Julien Deloffre, Benoı̂t Laignel and Nasre-Dine Ahfir and has published in prestigious journals such as The Science of The Total Environment, Water Resources Research and Journal of Hydrology.

In The Last Decade

M.T. Vu

16 papers receiving 317 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M.T. Vu France 9 191 122 87 83 56 19 322
Amalia Kokkinaki United States 12 247 1.3× 194 1.6× 105 1.2× 38 0.5× 27 0.5× 20 351
Martina Siena Italy 11 189 1.0× 142 1.2× 36 0.4× 21 0.3× 28 0.5× 21 309
Jaouher Kerrou Switzerland 9 242 1.3× 87 0.7× 66 0.8× 49 0.6× 18 0.3× 12 333
Francesca Boso United States 9 228 1.2× 97 0.8× 36 0.4× 20 0.2× 8 0.1× 15 349
Thomas Romary France 10 95 0.5× 77 0.6× 94 1.1× 25 0.3× 17 0.3× 33 288
G. P. Flach United States 10 136 0.7× 33 0.3× 34 0.4× 28 0.3× 22 0.4× 22 318
Sitakanta Mohanty United States 8 83 0.4× 72 0.6× 23 0.3× 46 0.6× 57 1.0× 19 356
Honggeun Jo United States 11 96 0.5× 283 2.3× 95 1.1× 14 0.2× 15 0.3× 27 436
Tom Clemo United States 10 298 1.6× 210 1.7× 233 2.7× 26 0.3× 11 0.2× 17 454
Chen Zuo China 10 57 0.3× 93 0.8× 48 0.6× 16 0.2× 7 0.1× 27 281

Countries citing papers authored by M.T. Vu

Since Specialization
Citations

This map shows the geographic impact of M.T. Vu'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 M.T. Vu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.T. Vu more than expected).

Fields of papers citing papers by M.T. Vu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.T. Vu. 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 M.T. Vu. The network helps show where M.T. Vu may publish in the future.

Co-authorship network of co-authors of M.T. Vu

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

All Works

19 of 19 papers shown
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Vu, M.T., Abderrahim Jardani, Nicolas Masséi, et al.. (2023). Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years. The Science of The Total Environment. 880. 163338–163338. 10 indexed citations
5.
Vu, M.T., et al.. (2023). Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system. The Science of The Total Environment. 897. 165494–165494. 11 indexed citations
6.
Nguyen, Son Truong, et al.. (2023). Excellent catalytic activity of NiPd/C nanocatalysts for anodic oxidation of methanol in alkaline media. IOP Conference Series Earth and Environmental Science. 1226(1). 12031–12031.
7.
Deloffre, Julien, et al.. (2023). Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine. Hydrological Sciences Journal. 68(10). 1372–1390. 2 indexed citations
8.
Vu, M.T. & Abderrahim Jardani. (2022). Mapping discrete fracture networks using inversion of hydraulic tomography data with convolutional neural network: SegNet-Fracture. Journal of Hydrology. 609. 127752–127752. 15 indexed citations
9.
Vu, M.T. & Abderrahim Jardani. (2022). Mapping of hydraulic transmissivity field from inversion of tracer test data using convolutional neural networks. CNN-2T. Journal of Hydrology. 606. 127443–127443. 17 indexed citations
10.
Vu, M.T. & Abderrahim Jardani. (2022). Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET. Journal of Hydrology. 612. 128167–128167. 8 indexed citations
11.
Vu, M.T., et al.. (2022). EFFECTS OF SYNTHESIS CONDITIONS ON STRUCTURE OF TIN NANORODS PREPARED BY SURFACTANT-ASSISTED CHEMICAL REDUCTION METHOD. Vietnam Journal of Science and Technology/Science and Technology. 59(6A). 168–175.
12.
Vu, M.T. & Abderrahim Jardani. (2021). Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT. Geophysical Journal International. 225(2). 1319–1331. 39 indexed citations
13.
Vu, M.T., et al.. (2020). Magnetometric resistivity tomography using chaos polynomial expansion. Geophysical Journal International. 221(3). 1469–1483. 2 indexed citations
14.
Jardani, Abderrahim, et al.. (2020). The use of electrical resistivity tomograms as a parameterization for the hydraulic characterization of a contaminated aquifer. Journal of Hydrology. 587. 124986–124986. 16 indexed citations
15.
Vu, M.T., Abderrahim Jardani, Nicolas Masséi, & Matthieu Fournier. (2020). Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network. Journal of Hydrology. 597. 125776–125776. 115 indexed citations
16.
Vu, M.T., et al.. (2019). Hydraulic tomography in time-lapse mode for tracking the clogging effects associated with the colloid injection. Advances in Water Resources. 133. 103424–103424. 4 indexed citations
17.
Vu, M.T. & P. M. Adler. (2014). Application of level-set method for deposition in three-dimensional reconstructed porous media. Physical Review E. 89(5). 53301–53301. 7 indexed citations
18.
Vu, M.T., et al.. (2013). Reactive transport in porous media: Pore-network model approach compared to pore-scale model. Physical Review E. 87(2). 23010–23010. 65 indexed citations
19.
Guillon, Sophie, M.T. Vu, Éric Pili, & P. M. Adler. (2013). Field and numerical determinations of pneumatic flow parameters of unsaturated fractured porous rocks on various scales. Water Resources Research. 49(5). 2801–2811. 5 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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