Md Galal Uddin

3.8k total citations · 6 hit papers
58 papers, 2.6k citations indexed

About

Md Galal Uddin is a scholar working on Water Science and Technology, Environmental Engineering and Geochemistry and Petrology. According to data from OpenAlex, Md Galal Uddin has authored 58 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Water Science and Technology, 31 papers in Environmental Engineering and 12 papers in Geochemistry and Petrology. Recurrent topics in Md Galal Uddin's work include Water Quality and Pollution Assessment (29 papers), Water Quality Monitoring Technologies (21 papers) and Hydrological Forecasting Using AI (20 papers). Md Galal Uddin is often cited by papers focused on Water Quality and Pollution Assessment (29 papers), Water Quality Monitoring Technologies (21 papers) and Hydrological Forecasting Using AI (20 papers). Md Galal Uddin collaborates with scholars based in Ireland, Australia and Bangladesh. Md Galal Uddin's co-authors include Agnieszka I. Olbert, Stephen Nash, Azizur Rahman, Mir Talas Mahammad Diganta, Md Moniruzzaman, Abdul Majed Sajib, Tomasz Dabrowski, Mohammad Abdul Quader, Firouzeh Taghikhah and Md. Moniruzzaman and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Water Research.

In The Last Decade

Md Galal Uddin

53 papers receiving 2.5k citations

Hit Papers

A review of water quality index models and their use for ... 2020 2026 2022 2024 2020 2022 2022 2022 2022 200 400 600
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. A review of water quality index models and their use for assessing surface water quality
    2020 736 citations Md Galal Uddin, Stephen Nash et al. profile →
  2. A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment
    2022 246 citations Md Galal Uddin, Stephen Nash et al. Water Research profile →
  3. Performance analysis of the water quality index model for predicting water state using machine learning techniques
    2022 175 citations Md Galal Uddin, Stephen Nash et al. profile →
  4. A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches
    2022 152 citations Md Galal Uddin, Stephen Nash et al. Water Research profile →
  5. Robust machine learning algorithms for predicting coastal water quality index
    2022 150 citations Md Galal Uddin, Stephen Nash et al. Journal of Environmental Management profile →
  6. Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches
    2024 50 citations Abdul Majed Sajib, Mir Talas Mahammad Diganta 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
Md Galal Uddin Ireland 23 1.9k 1.3k 539 464 190 58 2.6k
Agnieszka I. Olbert Ireland 26 1.8k 0.9× 1.2k 0.9× 460 0.9× 430 0.9× 443 2.3× 65 2.8k
Stephen Nash Ireland 25 1.6k 0.8× 1.1k 0.8× 367 0.7× 380 0.8× 393 2.1× 71 2.7k
Şehnaz Şener Türkiye 19 1.0k 0.5× 821 0.6× 693 1.3× 515 1.1× 256 1.3× 71 2.1k
Mohd Yawar Ali Khan Saudi Arabia 28 1.0k 0.5× 734 0.6× 493 0.9× 124 0.3× 495 2.6× 80 1.8k
Yen‐Lien Kuo Taiwan 14 1.2k 0.6× 746 0.6× 753 1.4× 284 0.6× 169 0.9× 42 2.0k
Mohd Kamil Yusoff Malaysia 19 886 0.5× 584 0.4× 263 0.5× 283 0.6× 156 0.8× 47 1.5k
David Russo Israel 38 1.1k 0.6× 3.2k 2.4× 468 0.9× 371 0.8× 438 2.3× 125 4.5k
Mohamed Gad Egypt 24 993 0.5× 933 0.7× 787 1.5× 141 0.3× 74 0.4× 51 1.6k
Ashwani Kumar Tiwari India 31 1.4k 0.7× 1.2k 0.9× 1.4k 2.7× 107 0.2× 200 1.1× 76 2.6k
Ajaykumar Kadam India 23 1.1k 0.6× 1.2k 0.9× 742 1.4× 132 0.3× 437 2.3× 43 1.8k

Countries citing papers authored by Md Galal Uddin

Since Specialization
Citations

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

Fields of papers citing papers by Md Galal Uddin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md Galal Uddin. 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 Md Galal Uddin. The network helps show where Md Galal Uddin may publish in the future.

Co-authorship network of co-authors of Md Galal Uddin

This figure shows the co-authorship network connecting the top 25 collaborators of Md Galal Uddin. A scholar is included among the top collaborators of Md Galal Uddin 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 Md Galal Uddin. Md Galal Uddin 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.
Uddin, Md Galal, et al.. (2026). Nowcasting CO2 emissions in Bangladesh: a machine learning approach. Discover Sustainability. 7(1).
2.
Uddin, Md Galal, et al.. (2025). The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model. Alexandria Engineering Journal. 122. 411–435. 4 indexed citations
3.
4.
Diganta, Mir Talas Mahammad, William M. Burke, Abdul Majed Sajib, et al.. (2025). Developing river water quality prediction model incorporating reliable indexing approach. Journal of Environmental Sciences. 164. 581–598.
5.
Sajib, Abdul Majed, et al.. (2025). Remote sensing applications for monitoring optically inactive water quality indicators: A comprehensive review. Earth-Science Reviews. 271. 105259–105259.
6.
Zhang, Yunhui, Zhanxue Sun, Xu Dong Zhang, et al.. (2025). A novel grid-based technique for quantifying groundwater quality under land use/land cover changes to support improved groundwater management. Journal of Hydrology. 662. 133955–133955. 3 indexed citations
7.
Mohammadpour, Amin, Mohammad Golaki, Soroush Abolfathi, et al.. (2025). Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis. Results in Engineering. 27. 105421–105421. 5 indexed citations
8.
Yang, Shiming, et al.. (2025). Quantitative source apportionment and health risk assessment of potentially toxic elements from the surface water and groundwater in a typical coal-mining area. Physics and Chemistry of the Earth Parts A/B/C. 140. 104008–104008. 1 indexed citations
9.
Zhang, Yunhui, et al.. (2024). Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China. Journal of Hazardous Materials. 479. 135663–135663. 22 indexed citations
10.
Olbert, Agnieszka I., et al.. (2024). AI-driven modelling approaches for predicting oxygen levels in aquatic environments. Journal of Water Process Engineering. 66. 105940–105940. 10 indexed citations
11.
12.
Ullah, Niamat, Aqil Tariq, Said Qasim, et al.. (2024). Geospatial analysis and AHP for flood risk mapping in Quetta, Pakistan: a tool for disaster management and mitigation. Applied Water Science. 14(11). 13 indexed citations
13.
Uddin, Md Galal, et al.. (2024). Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review. Heliyon. 10(17). e37073–e37073. 13 indexed citations
14.
Uddin, Md Galal, Stephen Nash, Azizur Rahman, Tomasz Dabrowski, & Agnieszka I. Olbert. (2023). Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches. Environmental Research. 242. 117755–117755. 41 indexed citations
15.
Uddin, Md Galal, Stephen Nash, Azizur Rahman, & Agnieszka I. Olbert. (2023). A sophisticated model for rating water quality. The Science of The Total Environment. 868. 161614–161614. 77 indexed citations
16.
Shaibur, Molla Rahman, Md. Shahidul Islam, Abu Shamim Khan, et al.. (2023). Groundwater Quality of Some Parts of Coastal Bhola District, Bangladesh: Exceptional Evidence. SHILAP Revista de lepidopterología. 7(3). 71–71. 37 indexed citations
17.
Uddin, Md Galal, Stephen Nash, Azizur Rahman, & Agnieszka I. Olbert. (2022). Assessing optimization techniques for improving water quality model. Journal of Cleaner Production. 385. 135671–135671. 106 indexed citations
18.
Uddin, Md Galal, Stephen Nash, Azizur Rahman, & Agnieszka I. Olbert. (2022). A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Research. 229. 119422–119422. 152 indexed citations breakdown →
19.
Uddin, Md Galal, Stephen Nash, Mir Talas Mahammad Diganta, Azizur Rahman, & Agnieszka I. Olbert. (2022). Robust machine learning algorithms for predicting coastal water quality index. Journal of Environmental Management. 321. 115923–115923. 150 indexed citations breakdown →
20.
Uddin, Md Galal, et al.. (2016). Seasonal variation of physicochemical properties of water in the Buriganga river, Bangladesh. World Applied Sciences Journal. 34(1). 24–34. 13 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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