T.M.K.G. Fernando
- Environmental Engineering top 2%
- Water Science and Technology top 5%
- Global and Planetary Change top 10%
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Co-authors
- Graeme C. DandyHolger R. MaierA. W. JayawardenaBellie SivakumarRobert J. MayR. MayNitin MuttilBarry Croke
- Topics
- Neural Networks and Applications (6 papers)Hydrological Forecasting Using AI (6 papers)Hydrology and Watershed Management Studies (3 papers)
- Journals
- Journal of HydrologyEnvironmental Modelling & SoftwareAdelaide Research & Scholarship (AR&S) (University of Adelaide)
In The Last Decade
T.M.K.G. Fernando
9 papers receiving 652 citations
Peers
Comparison fields: 5 of 88
- Environmental Engineering 435
- Water Science and Technology 320
- Global and Planetary Change 236
- Artificial Intelligence 132
- Electrical and Electronic Engineering 126
Countries citing papers authored by T.M.K.G. Fernando
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 135 | |
| 2 | 244 | |
| 3 | Assessing Prediction Uncertainty in the BIGMOD Model: A Shuffled Complex Evolution Metropolis Algorithm Approach | 3 |
| 4 | 5 | |
| 5 | Rainfall-runoff modelling using genetic programming | 14 |
| 6 | Efficient selection of inputs for artificial neural network models | 19 |
| 7 | 248 | |
| 8 | River flow prediction: an artificial neural network approach | 6 |
| 9 | Comparison of ANN, dynamical systems and support vector approaches for river discharge prediction | 3 |
About T.M.K.G. Fernando
T.M.K.G. Fernando is a scholar working on Environmental Engineering, Artificial Intelligence and Water Science and Technology, having authored 9 papers that have together received 677 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Hydrological Forecasting Using AI (6 papers) and Hydrology and Watershed Management Studies (3 papers). The work is most often cited by research in Environmental Engineering (435 citations), Water Science and Technology (320 citations) and Global and Planetary Change (236 citations). T.M.K.G. Fernando has collaborated with scholars based in Australia and Hong Kong. Frequent co-authors include Graeme C. Dandy, Holger R. Maier, A. W. Jayawardena, Bellie Sivakumar, Robert J. May, R. May, Nitin Muttil and Barry Croke. Their work appears in journals such as Journal of Hydrology, Environmental Modelling & Software and Adelaide Research & Scholarship (AR&S) (University of Adelaide).
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.