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.
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
20001.9k citationsHolger R. Maier, Graeme C. Dandyprofile →
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
2010697 citationsHolger R. Maier, Graeme C. Dandy et al.profile →
Genetic Algorithms Compared to Other Techniques for Pipe Optimization
1994555 citationsAngus R. Simpson, Graeme C. Dandy et al.profile →
An Improved Genetic Algorithm for Pipe Network Optimization
1996385 citationsGraeme C. Dandy, Angus R. Simpson et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Graeme C. Dandy
Since
Specialization
Citations
This map shows the geographic impact of Graeme C. Dandy'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 Graeme C. Dandy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Graeme C. Dandy more than expected).
This network shows the impact of papers produced by Graeme C. Dandy. 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 Graeme C. Dandy. The network helps show where Graeme C. Dandy may publish in the future.
Co-authorship network of co-authors of Graeme C. Dandy
This figure shows the co-authorship network connecting the top 25 collaborators of Graeme C. Dandy.
A scholar is included among the top collaborators of Graeme C. Dandy 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 Graeme C. Dandy. Graeme C. Dandy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wu, Wenyan, et al.. (2012). Battle of the water networks ii: Combining engineering judgement with genetic algorithm optimisation. 77.6 indexed citations
6.
Dandy, Graeme C., et al.. (2012). A comparative study between three genetic algorithm software applications for optimizing water distribution systems. 1306.1 indexed citations
7.
Bi, Weiwei & Graeme C. Dandy. (2012). Retraining of metamodels for the optimisation of water distribution systems. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 36.1 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
Iseri, Yoshihiko, Graeme C. Dandy, Holger R. Maier, Atsuo Kawamura, & Kenji Jinno. (2005). Medium term forecasting of rainfall using artificial neural networks. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1834–1840.11 indexed citations
15.
Ravalico, J.K., Holger R. Maier, Graeme C. Dandy, J.P. Norton, & Barry Croke. (2005). A comparison of sensitivity analysis techniques for complex models for environmental management. ANU Open Research (Australian National University).27 indexed citations
16.
Norton, J.P., Francis H. S. Chiew, Graeme C. Dandy, & Holger R. Maier. (2005). A parameter-bounding approach to sensitivity assessment of large simulation models. Adelaide Research & Scholarship (AR&S) (University of Adelaide).3 indexed citations
Maier, Holger R. & Graeme C. Dandy. (1996). Neural Network Models for Forecasting Univariate Time Series. Adelaide Research & Scholarship (AR&S) (University of Adelaide).29 indexed citations
19.
Dandy, Graeme C., et al.. (1995). Use of Genetic Algorithms for Project Sequencing. 540–543.1 indexed citations
20.
Simpson, Angus R., et al.. (1993). Pipe network optimisation using genetic algorithms. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 392–395.5 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.