World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
16946
World Ranking
4709
National Ranking
141

Graeme C. Dandy publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Graeme C. Dandy sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 190 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Graeme C. Dandy D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Graeme C. Dandy sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Graeme C. Dandy is affiliated with the University of Adelaide in Australia, focusing research in Environmental Science and Engineering. Their body of work addresses key aspects of water systems and optimization, with emphasis on flood risk assessment, water resources management, and urban stormwater management solutions.

Their research spans several main topics including:

  • Flood Risk Assessment and Management
  • Water resources management and optimization
  • Water Systems and Optimization
  • Urban Stormwater Management Solutions
  • Water-Energy-Food Nexus Studies
  • Hydrology and Watershed Management Studies
  • Reservoir Engineering and Simulation Methods

They have contributed to various subfields such as Water Science and Technology, Ocean Engineering, Global and Planetary Change, Civil and Structural Engineering, and Environmental Engineering.

Their frequent co-authors include Holger R. Maier, Wenyan Wu, Ruijie Liang, Mark Thyer, and Leila Eamen.

Publication venues commonly associated with their work are:

  • Journal of Hydrology
  • Journal of Water Resources Planning and Management
  • Water
  • MODSIM2021, 24th International Congress on Modelling and Simulation.
  • Environmental Modelling & Software

Recent papers authored or co-authored by Graeme C. Dandy include:

  • "The changing nature of the water-energy nexus in urban water supply systems: a critical review of changes and responses," 2020, Journal of Water and Climate Change
  • "Integrated Simulation-Optimization Framework for Water Allocation Based on Sustainability of Surface Water and Groundwater Resources," 2021, Journal of Water Resources Planning and Management
  • "Optimising the design and real-time operation of systems of distributed stormwater storages to reduce urban flooding at the catchment scale," 2021, Journal of Hydrology
  • "Beyond engineering: A review of reservoir management through the lens of wickedness, competing objectives and uncertainty," 2023, Environmental Modelling & Software
  • "A Review of Sources of Uncertainty in Optimization Objectives of Water Distribution Systems," 2022, Water

Best Publications

  • Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

    Holger R. Maier;Graeme C. Dandy

  • Genetic algorithms compared to other techniques for pipe optimization

    Angus R. Simpson;Graeme C. Dandy;Laurence J. Murphy

  • Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

    Holger R. Maier;Ashu Jain;Graeme C. Dandy;K. P. Sudheer

  • The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters

    Holger R. Maier;Graeme C. Dandy

  • Evolutionary algorithms and other metaheuristics in water resources

    H.R. Maier;Z. Kapelan;J. Kasprzyk;J. Kollat

  • An Improved Genetic Algorithm for Pipe Network Optimization

    Graeme C. Dandy;Angus R. Simpson;Laurence J. Murphy

  • Input determination for neural network models in water resources applications. Part 1—background and methodology

    Gavin J. Bowden;Graeme C. Dandy;Holger R. Maier

  • Review of Input Variable Selection Methods for Artificial Neural Networks

    Robert May;Graeme Dandy;Holger Maier

  • Optimal division of data for neural network models in water resources applications

    Gavin J. Bowden;Holger R. Maier;Graeme C. Dandy

  • Non-linear variable selection for artificial neural networks using partial mutual information

    Robert J. May;Holger R. Maier;Graeme C. Dandy;T.M.K. Gayani Fernando

  • Review: Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

    Wenyan Wu;Graeme C. Dandy;Holger R. Maier

  • The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study

    Holger R. Maier;Graeme C. Dandy

  • Data splitting for artificial neural networks using SOM-based stratified sampling.

    Robert J. May;Holger R. Maier;Graeme C. Dandy

  • A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    Greer B. Humphrey;Matthew S. Gibbs;Graeme C. Dandy;Holger R. Maier

  • Neural network based modelling of environmental variables: A systematic approach

    H. R. Maier;G. C. Dandy

  • Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river

    Gavin J. Bowden;Holger R. Maier;Graeme C. Dandy

  • Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia

    Holger R Maier;Graeme C Dandy;Michael D Burch

  • An evaluation framework for input variable selection algorithms for environmental data-driven models

    Stefano Galelli;Greer B. Humphrey;Holger R. Maier;Andrea Castelletti

  • Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems

    Robert J. May;Graeme C. Dandy;Holger R. Maier;John B. Nixon

  • Water Distribution System Optimization Using Metamodels

    D. R. Broad;G. C. Dandy;H. R. Maier

  • Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach

    T.M.K.G. Fernando;H.R. Maier;G.C. Dandy

Frequent Co-Authors

Holger R. Maier
Holger R. Maier University of Adelaide
Angus R. Simpson
Angus R. Simpson University of Adelaide
Aaron C. Zecchin
Aaron C. Zecchin University of Adelaide
Zoran Kapelan
Zoran Kapelan Delft University of Technology
Craig T. Simmons
Craig T. Simmons University of Newcastle Australia
Adrian D. Werner
Adrian D. Werner Flinders University
Philip Brunner
Philip Brunner University of Neuchâtel
Edward A. McBean
Edward A. McBean University of Guelph
René Therrien
René Therrien Université Laval
Andrea Castelletti
Andrea Castelletti Polytechnic University of Milan

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