T.M.K.G. Fernando

855 total citations
9 papers, 677 citations indexed

About

T.M.K.G. Fernando is a scholar working on Artificial Intelligence, Environmental Engineering and Water Science and Technology. According to data from OpenAlex, T.M.K.G. Fernando has authored 9 papers receiving a total of 677 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Environmental Engineering and 3 papers in Water Science and Technology. Recurrent topics in T.M.K.G. Fernando's work include Neural Networks and Applications (6 papers), Hydrological Forecasting Using AI (6 papers) and Hydrology and Watershed Management Studies (3 papers). T.M.K.G. Fernando is often cited by papers focused on Neural Networks and Applications (6 papers), Hydrological Forecasting Using AI (6 papers) and Hydrology and Watershed Management Studies (3 papers). T.M.K.G. Fernando collaborates with scholars based in Australia and Hong Kong. T.M.K.G. Fernando's co-authors include Holger R. Maier, Graeme C. Dandy, A. W. Jayawardena, Bellie Sivakumar, Robert J. May, R. May, Nitin Muttil and Barry Croke and has published in prestigious journals such as Journal of Hydrology, Environmental Modelling & Software and Adelaide Research & Scholarship (AR&S) (University of Adelaide).

In The Last Decade

T.M.K.G. Fernando

9 papers receiving 652 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T.M.K.G. Fernando Australia	6	435	320	236	132	126	9	677
Pin-An Chen Taiwan	8	333 0.8×	290 0.9×	236 1.0×	80 0.6×	71 0.6×	9	563
Youngmin Seo South Korea	16	589 1.4×	389 1.2×	281 1.2×	147 1.1×	226 1.8×	38	878
Abinash Sahoo India	21	476 1.1×	373 1.2×	368 1.6×	102 0.8×	128 1.0×	42	867
Vahid Karimi Iran	13	514 1.2×	317 1.0×	266 1.1×	102 0.8×	142 1.1×	21	808
Roozbeh Moazenzadeh Iran	11	339 0.8×	248 0.8×	259 1.1×	138 1.0×	95 0.8×	15	707
Robert J. May Australia	6	335 0.8×	252 0.8×	169 0.7×	88 0.7×	63 0.5×	9	565
Slavco Velickov Netherlands	6	299 0.7×	211 0.7×	146 0.6×	80 0.6×	75 0.6×	9	612
I‐Fan Chang Taiwan	4	285 0.7×	169 0.5×	166 0.7×	55 0.4×	106 0.8×	11	520
Akram Seifi Iran	14	415 1.0×	325 1.0×	168 0.7×	103 0.8×	90 0.7×	28	720
A. Najah Malaysia	11	565 1.3×	469 1.5×	175 0.7×	126 1.0×	106 0.8×	16	801

Countries citing papers authored by T.M.K.G. Fernando

Since Specialization

Citations

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

Fields of papers citing papers by T.M.K.G. Fernando

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T.M.K.G. Fernando

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

All Works

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9 of 9 papers shown

Fernando, T.M.K.G., Holger R. Maier, & Graeme C. Dandy. (2008). Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. Journal of Hydrology. 367(3-4). 165–176. 135 indexed citations

May, Robert J., Holger R. Maier, Graeme C. Dandy, & T.M.K.G. Fernando. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software. 23(10-11). 1312–1326. 244 indexed citations

Fernando, T.M.K.G., Holger R. Maier, Graeme C. Dandy, & Barry Croke. (2007). Assessing Prediction Uncertainty in the BIGMOD Model: A Shuffled Complex Evolution Metropolis Algorithm Approach. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 3 indexed citations

May, R., Graeme C. Dandy, Holger R. Maier, & T.M.K.G. Fernando. (2006). Critical Values of a Kernel Density-based Mutual Information Estimator. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 4898–4903. 5 indexed citations

Muttil, Nitin, et al.. (2005). Rainfall-runoff modelling using genetic programming. Victoria University Research Repository (Victoria University). 14 indexed citations

Fernando, T.M.K.G., Holger R. Maier, Graeme C. Dandy, & R. May. (2005). Efficient selection of inputs for artificial neural network models. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 19 indexed citations

Sivakumar, Bellie, A. W. Jayawardena, & T.M.K.G. Fernando. (2002). River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology. 265(1-4). 225–245. 248 indexed citations

Jayawardena, A. W. & T.M.K.G. Fernando. (2001). River flow prediction: an artificial neural network approach. IAHS-AISH publication. 239–245. 6 indexed citations

Fernando, T.M.K.G., et al.. (2000). Comparison of ANN, dynamical systems and support vector approaches for river discharge prediction. 3 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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