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Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

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

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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