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Rising Stars

D-Index
40
Citations
3549
World Ranking
675
National Ranking
99

Computer Science

D-Index
39
Citations
3908
World Ranking
9913
National Ranking
4162

Sinan Q. Salih 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 Sinan Q. Salih 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: 74 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Sinan Q. Salih 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 Sinan Q. Salih 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: 39 D-Index — 33rd percentile

33% 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 Rising Stars Award

Overview

Sinan Q. Salih is affiliated with the IEEE Computer Society in the United States. Their research primarily spans the fields of Engineering, Environmental Science, and Computer Science, with particular focus on subfields such as Artificial Intelligence, Environmental Engineering, Mechanical Engineering, Electrical and Electronic Engineering, and Water Science and Technology.

The scientist's work covers a range of topics, with notable emphasis on Hydrological Forecasting Using AI, Hydrology and Watershed Management Studies, Energy Load and Power Forecasting, Solar Radiation and Photovoltaics, Climate Variability and Models, Machine Learning and Extreme Learning Machines (ELM), and Meteorological Phenomena and Simulations.

Recent publications by Sinan Q. Salih demonstrate an engagement with interdisciplinary approaches blending artificial intelligence with engineering and environmental sciences. Selected papers include:

  • Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation, 2020, IEEE Access
  • Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model, 2020, Sustainability
  • Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination, 2020, Journal of Hydrology
  • Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model, 2020, Engineering Applications of Computational Fluid Mechanics
  • Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model, 2020, Engineering With Computers

Frequent co-authors include:

  • Zaher Mundher Yaseen
  • Tao Hai
  • Nadhir Al-Ansari
  • Salem Alkhalaf
  • Anurag Malik

Sinan Q. Salih has published multiple works in various reputable venues, with a concentration in:

  • IEEE Access
  • Case Studies in Thermal Engineering
  • Work
  • Complexity
  • Energy Reports

Best Publications

  • Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

    Minglei Fu;Tingchao Fan;Zi'ang Ding;Sinan Q. Salih

  • Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

    Zaher Mundher Yaseen;Zainab Hasan Ali;Sinan Q. Salih;Nadhir Al-Ansari

  • Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation

    Zainab Abdulelah Al-Sudani;Sinan Q. Salih;Ahmad sharafati;Zaher Mundher Yaseen

  • A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

    Sinan Q. Salih;Sinan Q. Salih;AbdulRahman A. Alsewari

  • Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination

    S.I. Abba;Sinan Jasim Hadi;Saad Sh. Sammen;Sinan Q. Salih;Sinan Q. Salih

  • Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

    Anurag Malik;Anil Kumar;Sungwon Kim;Mahsa H. Kashani

  • River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

    Sinan Q. Salih;Ahmad Sharafati;Khabat Khosravi;Hossam Faris

  • Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions

    Wang Jing;Zaher Mundher Yaseen;Shamsuddin Shahid;Mandeep Kaur Saggi

  • 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

  • Thin and sharp edges bodies-fluid interaction simulation using cut-cell immersed boundary method

    Sinan Q. Salih;Mohammed Suleman Aldlemy;Mohammad Rasidi Rasani;A. K. Ariffin

  • Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

    Guangnan Zhang;Zainab Hasan Ali;Mohammed Suleman Aldlemy;Mohamed H. Mussa

  • Prediction of evaporation in arid and semi-arid regions : a comparative study using different machine learning models

    Zaher Mundher Yaseen;Anas Mahmood Al-Juboori;Ufuk Beyaztas;Nadhir Al-Ansari

  • Non-Linear Input Variable Selection Approach Integrated With Non-Tuned Data Intelligence Model for Streamflow Pattern Simulation

    Sinan Jasim Hadi;Sani Isah Abba;Saad Sh. Sammen;Sinan Q. Salih

  • Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model

    Abeer A. Al-Musawi;Afrah A. H. Alwanas;Sinan Q. Salih;Zainab Hasan Ali

  • Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model

    Hai Tao;Ahmed A. Ewees;Ahmed A. Ewees;Ali Omran Al-Sulttani;Ufuk Beyaztas

  • An Enhanced Version of Black Hole Algorithm via Levy Flight for Optimization and Data Clustering Problems

    Haneen A. Abdulwahab;Ahmad Noraziah;Abdul Rahman Ahmed Mohammed Al-Sewari;Sinan Q. Salih

  • Load-carrying capacity and mode failure simulation of beam-column joint connection: application of self-tuning machine learning model

    Afrah Abdulelah Hamzah Alwanas;Abeer A. Al-Musawi;Sinan Q. Salih;Hai Tao

  • Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms

    Zaher Mundher Yaseen;Sujay Raghavendra Naganna;Zulfaqar Sa’adi;Pijush Samui

  • Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model

    Tao Hai;Ahmad Sharafati;Achite Mohammed;Sinan Q. Salih

  • Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization.

    Mohammad Ehteram;Sinan Q. Salih;Zaher Mundher Yaseen

  • Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting.

    Haitham Abdulmohsin Afan;Mohammed Falah Allawi;Amr El-Shafie;Zaher Mundher Yaseen

Frequent Co-Authors

Zaher Mundher Yaseen
Zaher Mundher Yaseen King Fahd University of Petroleum and Minerals
Nadhir Al-Ansari
Nadhir Al-Ansari Luleå University of Technology
Shamsuddin Shahid
Shamsuddin Shahid University of Technology Malaysia
Ozgur Kisi
Ozgur Kisi Ilia State University
Kwok-wing Chau
Kwok-wing Chau Hong Kong Polytechnic University
Mumtaz Ali
Mumtaz Ali University of Southern Queensland
Ahmed A. Ewees
Ahmed A. Ewees Damietta University
Vijay P. Singh
Vijay P. Singh Texas A&M University
Zakirul Alam Bhuiyan
Zakirul Alam Bhuiyan Fordham University
Isa Ebtehaj
Isa Ebtehaj Université Laval

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