S. Y. Liong

796 total citations
28 papers, 607 citations indexed

About

S. Y. Liong is a scholar working on Global and Planetary Change, Water Science and Technology and Environmental Engineering. According to data from OpenAlex, S. Y. Liong has authored 28 papers receiving a total of 607 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Global and Planetary Change, 13 papers in Water Science and Technology and 11 papers in Environmental Engineering. Recurrent topics in S. Y. Liong's work include Hydrology and Watershed Management Studies (13 papers), Flood Risk Assessment and Management (10 papers) and Climate variability and models (10 papers). S. Y. Liong is often cited by papers focused on Hydrology and Watershed Management Studies (13 papers), Flood Risk Assessment and Management (10 papers) and Climate variability and models (10 papers). S. Y. Liong collaborates with scholars based in Singapore, Australia and France. S. Y. Liong's co-authors include Srivatsan V. Raghavan, Weng Tat Chan, Kok‐Kwang Phoon, C.Y. Liaw, V. Raghavan, Bellie Sivakumar, Xixi Lu, Xiaosheng Qin, Markus Disse and Harri Koivusalo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hydrology and International Journal of Climatology.

In The Last Decade

S. Y. Liong

28 papers receiving 585 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Y. Liong Singapore 15 396 298 196 120 50 28 607
Zoubeïda Bargaoui Tunisia 15 473 1.2× 328 1.1× 196 1.0× 231 1.9× 31 0.6× 48 702
Tao-Chang Yang Taiwan 13 424 1.1× 263 0.9× 290 1.5× 173 1.4× 65 1.3× 18 697
Edward P. Campbell Australia 10 424 1.1× 330 1.1× 213 1.1× 141 1.2× 72 1.4× 14 640
Yavuz Selim Güçlü Türkiye 14 678 1.7× 291 1.0× 209 1.1× 208 1.7× 26 0.5× 30 930
Schalk Jan van Andel Netherlands 15 540 1.4× 416 1.4× 193 1.0× 234 1.9× 78 1.6× 34 770
Zhenxiang Xing China 10 273 0.7× 204 0.7× 189 1.0× 69 0.6× 79 1.6× 49 583
Raj Mehrotra Australia 13 401 1.0× 264 0.9× 150 0.8× 136 1.1× 59 1.2× 19 643
D. Nalley Canada 8 523 1.3× 292 1.0× 320 1.6× 206 1.7× 22 0.4× 13 760
Babak Vaheddoost Türkiye 17 653 1.6× 373 1.3× 254 1.3× 92 0.8× 85 1.7× 56 917
Ilaria Prosdocimi United Kingdom 16 797 2.0× 522 1.8× 150 0.8× 143 1.2× 49 1.0× 38 967

Countries citing papers authored by S. Y. Liong

Since Specialization
Citations

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

Fields of papers citing papers by S. Y. Liong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Y. Liong. 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 S. Y. Liong. The network helps show where S. Y. Liong may publish in the future.

Co-authorship network of co-authors of S. Y. Liong

This figure shows the co-authorship network connecting the top 25 collaborators of S. Y. Liong. A scholar is included among the top collaborators of S. Y. Liong 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 S. Y. Liong. S. Y. Liong 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.
Raghavan, Srivatsan V., et al.. (2022). Application of Multi-Channel Convolutional Neural Network to Improve DEM Data in Urban Cities. SHILAP Revista de lepidopterología. 10(3). 61–61. 7 indexed citations
2.
Wendi, Dadiyorto, et al.. (2018). Projected impacts of climate change on stream flow and groundwater of Nee Soon freshwater swamp forest, Singapore. Gardens’ Bulletin Singapore. 70(1). 175–190. 10 indexed citations
3.
Corlett, Richard T., S. Y. Liong, Rudolf Meier, et al.. (2018). The biological, ecological and conservation significance of freshwater swamp forest in Singapore. Gardens’ Bulletin Singapore. 70(1). 9–31. 21 indexed citations
4.
Davison, Geoffrey, et al.. (2018). Conservation outputs and recommendations for Nee Soon freshwater swamp forest, Singapore. Gardens’ Bulletin Singapore. 70(1). 191–217. 6 indexed citations
5.
Vo, Ngoc Duong, et al.. (2017). Hydro-meteorological drought assessment under climate change impact over the Vu Gia–Thu Bon river basin, Vietnam. Hydrological Sciences Journal. 62(10). 1654–1668. 26 indexed citations
6.
Raghavan, V., et al.. (2016). Deriving short-duration rainfall IDF curves from a regional climate model. Natural Hazards. 85(3). 1877–1891. 24 indexed citations
7.
Raghavan, Srivatsan V., et al.. (2016). Ensemble climate projections of mean and extreme rainfall over Vietnam. Global and Planetary Change. 148. 96–104. 22 indexed citations
8.
Raghavan, Srivatsan V., et al.. (2016). Use of Regional Climate Models for Proxy Data over Transboundary Regions. Journal of Hydrologic Engineering. 21(6). 8 indexed citations
9.
Gan, Thian Yew, Stephan Hülsmann, Xiaosheng Qin, et al.. (2015). Possible climate change/variability and human impacts, vulnerability of drought-prone regions, water resources and capacity building for Africa. Hydrological Sciences Journal. 1–18. 73 indexed citations
10.
Raghavan, V., et al.. (2015). Ensemble Climate Projection for Hydro-Meteorological Drought over a river basin in Central Highland, Vietnam. KSCE Journal of Civil Engineering. 19(2). 427–433. 33 indexed citations
11.
Raghavan, Srivatsan V., et al.. (2012). SWAT use of gridded observations for simulating runoff – a Vietnam river basin study. Hydrology and earth system sciences. 16(8). 2801–2811. 82 indexed citations
12.
Raghavan, Srivatsan V., et al.. (2011). Simulating stream flow over data sparse areas – an application of internet based data. 1 indexed citations
13.
Verwey, A., Nitin Muttil, S. Y. Liong, & Shanshan He. (2008). Implementing an Urban Rainfall-runoff Concept in SOBEK for a Catchment in Singapore. Victoria University Research Repository (Victoria University). 36. 1 indexed citations
14.
Liong, S. Y., et al.. (2005). Derivation of effective and efficient data set with subtractive clustering method and genetic algorithm. Journal of Hydroinformatics. 7(4). 219–233. 16 indexed citations
15.
Liong, S. Y., et al.. (2001). Forecasting of river flow data with a general regression neural network. IAHS-AISH publication. 285–290. 8 indexed citations
16.
Muttil, Nitin & S. Y. Liong. (2001). Improving Runoff Forecasting by Input Variable Selection in Genetic Programming. National University of Singapore. 1–7. 14 indexed citations
17.
Liong, S. Y., et al.. (2001). Determination of Optimal and Stable Prediction Parameters Values in Chaotic Time Series. National University of Singapore. 1–9. 1 indexed citations
18.
Sivakumar, Bellie, Kok‐Kwang Phoon, S. Y. Liong, & C.Y. Liaw. (1999). A systematic approach to noise reduction in chaotic hydrological time series. Journal of Hydrology. 219(3-4). 103–135. 60 indexed citations
19.
Liong, S. Y., et al.. (1991). Computer-aided dam break flow routing. 13(3). 110–115. 1 indexed citations
20.
Liong, S. Y., et al.. (1989). Roughness Values for Overland Flow in Subcatchments. Journal of Irrigation and Drainage Engineering. 115(2). 203–214. 12 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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