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Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

D-Index
57
Citations
13007
World Ranking
3844
National Ranking
45

Dimitri Solomatine 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 Dimitri Solomatine 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: 239 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Dimitri Solomatine 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 Dimitri Solomatine 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award

Overview

Dimitri Solomatine is affiliated with the IHE Delft Institute for Water Education in the Netherlands. Their research focus covers several aspects of environmental science and engineering, with a particular emphasis on water-related studies.

The main fields of study for Dimitri Solomatine include:

  • Environmental Science
  • Engineering

Within these broader fields, their work addresses several subfields such as:

  • Global and Planetary Change
  • Water Science and Technology
  • Environmental Engineering
  • Ocean Engineering
  • Atmospheric Science

Key topics frequently explored in their publications include:

  • Hydrology and Watershed Management Studies
  • Flood Risk Assessment and Management
  • Hydrological Forecasting Using AI
  • Hydrology and Drought Analysis
  • Water resources management and optimization
  • Precipitation Measurement and Analysis
  • Reservoir Engineering and Simulation Methods

Dimitri Solomatine has contributed multiple papers within notable publication venues, including:

  • Special publications
  • Water
  • SSRN Electronic Journal
  • Environmental Modelling & Software
  • Journal of Hydrology

Examples of recent papers by Dimitri Solomatine include:

  • Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning, 2020, Geophysical Research Letters
  • A comprehensive review on the design and optimization of surface water quality monitoring networks, 2020, Environmental Modelling & Software
  • An approach to characterise spatio-temporal drought dynamics, 2020, Advances in Water Resources
  • Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: A perspective, 2022, Hydrological Processes
  • Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning, 2021, Journal of Hydrology

The scientist frequently collaborates with several co-authors, among them:

  • Gerald Corzo
  • Gerald A. Corzo Perez
  • Andréja Jonoski
  • R. Uijlenhoet
  • Shreedhar Maskey

Best Publications

  • Data-driven modelling: some past experiences and new approaches

    Dimitri P. Solomatine;Avi Ostfeld

  • Model Induction with Support Vector Machines: Introduction and Applications

    Yonas B. Dibike;Slavco Velickov;Dimitri Solomatine;Michael B. Abbott

  • Evolutionary algorithms and other metaheuristics in water resources

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

  • Model trees as an alternative to neural networks in rainfall-runoff modelling

    Dimitri P. Solomatine;Khada N. Dulal

  • 2006 Special issue: Machine learning approaches for estimation of prediction interval for the model output

    Durga L. Shrestha;Dimitri P. Solomatine

  • Neural networks and M5 model trees in modelling water level-discharge relationship

    B. Bhattacharya;D. P. Solomatine

  • M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China

    Dimitri P. Solomatine;Yunpeng Xue

  • AdaBoost.RT: a boosting algorithm for regression problems

    D.P. Solomatine;D.L. Shrestha

  • Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

    Robert J. Abrahart;François Anctil;Paulin Coulibaly;Christian W. Dawson

  • River flow forecasting using artificial neural networks

    Y.B. Dibike;D.P. Solomatine

  • Data-Driven Modelling: Concepts, Approaches and Experiences

    D. Solomatine

  • Machine Learning Approach to Modeling Sediment Transport

    B. Bhattacharya;R. K. Price;D. P. Solomatine

  • Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

    Shijie Jiang;Shijie Jiang;Yi Zheng;Dimitri Solomatine;Dimitri Solomatine;Dimitri Solomatine

  • A framework for uncertainty analysis in flood risk management decisions

    Jim Hall;Dimitri Solomatine

  • Experiments with AdaBoost.RT, an improved boosting scheme for regression

    D. L. Shrestha;D. P. Solomatine

  • A novel method to estimate model uncertainty using machine learning techniques

    Dimitri P. Solomatine;Dimitri P. Solomatine;Durga Lal Shrestha

  • River cross-section extraction from the ASTER global DEM for flood modeling

    T. Z. Gichamo;I. Popescu;A. Jonoski;D. Solomatine

  • Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

    A. Elshorbagy;G. Corzo;S. Srinivasulu;D. P. Solomatine;D. P. Solomatine

  • Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    A. Elshorbagy;G. Corzo;S. Srinivasulu;D. P. Solomatine;D. P. Solomatine

  • Citizen observations contributing to flood modelling: opportunities and challenges

    Thaine Herman Assumpção;Ioana Popescu;Andreja Jonoski;Dimitri P. Solomatine

  • On the encapsulation of numerical-hydraulic models in artificial neural network

    Yonas B. Dibike;Dimitri Solomatine;Michael B. abbott

Frequent Co-Authors

Giuliano Di Baldassarre
Giuliano Di Baldassarre Uppsala University
Albrecht Weerts
Albrecht Weerts Wageningen University & Research
Stefan Uhlenbrook
Stefan Uhlenbrook International Water Management Institute
Holger R. Maier
Holger R. Maier University of Adelaide
Amin Elshorbagy
Amin Elshorbagy University of Saskatchewan
Shreedhar Maskey
Shreedhar Maskey IHE Delft Institute for Water Education
Vladimir Cherkassky
Vladimir Cherkassky University of Minnesota
Linda See
Linda See International Institute for Applied Systems Analysis
Avi Ostfeld
Avi Ostfeld Technion – Israel Institute of Technology
Dong Jun Seo
Dong Jun Seo The University of Texas at Arlington

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