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2025

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Rising Stars

D-Index
46
Citations
5428
World Ranking
431
National Ranking
12

Engineering and Technology

D-Index
51
Citations
6160
World Ranking
3983
National Ranking
160

Isa Ebtehaj publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Isa Ebtehaj sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 157 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Isa Ebtehaj D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Isa Ebtehaj sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 51 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Isa Ebtehaj is affiliated with Université Laval in Canada and specializes in environmental science and engineering with a notable focus on hydrological studies. Their research covers a range of topics including hydrological forecasting using artificial intelligence, watershed management, hydraulic structures, flood risk assessment, and sediment transport processes.

The scientist's body of work spans multiple subfields such as environmental engineering, global and planetary change, water science and technology, artificial intelligence, and civil and structural engineering.

Key topics in Isa Ebtehaj's research include:

  • Hydrological Forecasting Using AI
  • Hydrology and Watershed Management Studies
  • Hydraulic flow and structures
  • Flood Risk Assessment and Management
  • Machine Learning and ELM
  • Hydrology and Sediment Transport Processes
  • Hydrology and Drought Analysis

Their research has been published extensively in several scientific venues, with frequent contributions to:

  • Journal of Hydrologic Engineering
  • Journal of Irrigation and Drainage Engineering
  • Journal of Hydrology
  • Hydrology
  • The Science of The Total Environment

Isa Ebtehaj has collaborated regularly with several co-authors including Hossein Bonakdari, Bahram Gharabaghi, Silvio José Gumière, Joseph D. Ladouceur, and Afshin Amiri. These collaborations have contributed to a dynamic research network within the environmental and engineering domains.

Recent published papers highlight the diverse scope of their work:

  • "Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future", 2021, The Science of The Total Environment
  • "Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models", 2021, Engineering Applications of Computational Fluid Mechanics
  • "A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems", 2024, Journal of Hydrology
  • "Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments", 2020, Hydrological Sciences Journal
  • "Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture", 2020, Computers and Electronics in Agriculture

Best Publications

  • Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

    Zaher Mundher Yaseen;Zaher Mundher Yaseen;Isa Ebtehaj;Hossein Bonakdari;Ravinesh C. Deo

  • Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

    Mohammad Ali Ghorbani;Mohammad Ali Ghorbani;Shahaboddin Shamshirband;Davoud Zare Haghi;Atefe Azani

  • GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs

    Isa Ebtehaj;Hossein Bonakdari;Amir Hossein Zaji;Hamed Azimi

  • Gene expression programming to predict the discharge coefficient in rectangular side weirs

    Isa Ebtehaj;Hossein Bonakdari;Amir Hossein Zaji;Hamed Azimi

  • EVALUATION OF SEDIMENT TRANSPORT IN SEWER USING ARTIFICIAL NEURAL NETWORK

    Isa Ebtehaj;Hossein Bonakdari

  • Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

    Zaher Mundher Yaseen;Zaher Mundher Yaseen;Mazen Ismaeel Ghareb;Isa Ebtehaj;Hossein Bonakdari

  • Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers

    Isa Ebtehaj;Hossein Bonakdari

  • An integrated framework of Extreme Learning Machines for predicting scour at pile groups in clear water condition

    Isa Ebtehaj;Hossein Bonakdari;Fatemeh Moradi;Bahram Gharabaghi

  • Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology.

    Khadije Lotfi;Hossein Bonakdari;Isa Ebtehaj;Farouq S. Mjalli

  • Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis

    Zaher Mundher Yaseen;Isa Ebtehaj;Sungwon Kim;Hadi Sanikhani

  • Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design

    Saba Shaghaghi;Hossein Bonakdari;Azadeh Gholami;Isa Ebtehaj

  • Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region

    Zaher Mundher Yaseen;Wan Hanna Melini Wan Mohtar;Ameen Mohammed Salih Ameen;Isa Ebtehaj

  • Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

    Fatemeh Khoshbin;Hossein Bonakdari;Seyed Hamed Ashraf Talesh;Isa Ebtehaj

  • Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate.

    Mohammad Zeynoddin;Hossein Bonakdari;Arash Azari;Isa Ebtehaj

  • Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling

    Isa Ebtehaj;Hossein Bonakdari;Bahram Gharabaghi

  • Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices

    Isa Ebtehaj;Hossein Bonakdari;Fatemeh Khoshbin;Hamed Azimi

  • Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine

    Hossein Bonakdari;Isa Ebtehaj;Pijush Samui;Bahram Gharabaghi

  • Design of radial basis function-based support vector regression in predicting the discharge coefficient of a side weir in a trapezoidal channel

    Hamed Azimi;Hossein Bonakdari;Isa Ebtehaj

  • A reliable linear stochastic daily soil temperature forecast model

    Mohammad Zeynoddin;Hossein Bonakdari;Isa Ebtehaj;Fatemeh Esmaeilbeiki

  • Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing

    Azadeh Gholami;Hossein Bonakdari;Isa Ebtehaj;Majid Mohammadian

  • Extreme learning machine assessment for estimating sediment transport in open channels

    Isa Ebtehaj;Hossein Bonakdari;Shahaboddin Shamshirband

  • Design criteria for sediment transport in sewers based on self-cleansing concept

    Isa Ebtehaj;Hossein Bonakdari;Ali Sharifi

Frequent Co-Authors

Hossein Bonakdari
Hossein Bonakdari University of Ottawa
Bahram Gharabaghi
Bahram Gharabaghi University of Guelph
Zaher Mundher Yaseen
Zaher Mundher Yaseen King Fahd University of Petroleum and Minerals
Amir Mosavi
Amir Mosavi Óbuda University
Ravinesh C. Deo
Ravinesh C. Deo University of Southern Queensland
Shahab S. Band
Shahab S. Band National Yunlin University of Science and Technology
Ali Akbar Zinatizadeh
Ali Akbar Zinatizadeh Razi University
Nadhir Al-Ansari
Nadhir Al-Ansari Luleå University of Technology
Shamsuddin Shahid
Shamsuddin Shahid University of Technology Malaysia
Pijush Samui
Pijush Samui National Institute of Technology Patna

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