World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
10050
World Ranking
3498
National Ranking
23

Fi-John Chang 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 Fi-John Chang 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: 138 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Fi-John Chang 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 Fi-John Chang 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Fi-John Chang is affiliated with National Taiwan University in Taiwan and has contributed extensively to environmental science and engineering. Their research spans areas such as flood risk assessment, hydrological forecasting using artificial intelligence, and air quality monitoring and forecasting.

The main fields of study for Fi-John Chang include Environmental Science with 101 publications and Engineering with 37 publications. Their subfields cover Global and Planetary Change, Environmental Engineering, Water Science and Technology, Ocean Engineering, and Electrical and Electronic Engineering.

Frequent publication venues where Fi-John Chang's work appears include:

  • Journal of Hydrology
  • Water
  • Journal of Cleaner Production
  • Journal of Environmental Management
  • The Science of The Total Environment

Key topics addressed in their research are:

  • Flood Risk Assessment and Management
  • Hydrological Forecasting Using AI
  • Hydrology and Watershed Management Studies
  • Water-Energy-Food Nexus Studies
  • Water resources management and optimization
  • Air Quality Monitoring and Forecasting
  • Air Quality and Health Impacts

Some recent papers authored or co-authored by Fi-John Chang include:

  • "Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques," 2020, The Science of The Total Environment
  • "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting," 2020, Journal of Hydrology
  • "Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts," 2021, Journal of Hydrology
  • "Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting," 2020, Journal of Cleaner Production
  • "An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies," 2020, Applied Energy

Frequent collaborators include:

  • Yanlai Zhou
  • Li-Chiu Chang
  • Chong-Yu Xu
  • Pu-Yun Kow
  • Shenglian Guo

Best Publications

  • Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

    Fi-John Chang;Ya-Ting Chang

  • Optimizing the reservoir operating rule curves by genetic algorithms

    Fi-John Chang;Li Chen;Li-Chiu Chang

  • Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting

    I-Feng Kao;Yanlai Zhou;Li-Chiu Chang;Fi-John Chang

  • A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction

    Fi-John Chang;Yen-Chang Chen

  • Intelligent control for modelling of real‐time reservoir operation

    Li-Chiu Chang;Fi-John Chang

  • Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling

    Yen-Ming Chiang;Li-Chiu Chang;Fi-John Chang

  • Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

    Fi-John Chang;Pin-An Chen;Ying-Ray Lu;Eric Huang

  • HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

    Chaopeng Shen;Eric Laloy;Amin Elshorbagy;Adrian Albert

  • Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts

    Yanlai Zhou;Fi-John Chang;Li-Chiu Chang;I-Feng Kao

  • Multi-objective evolutionary algorithm for operating parallel reservoir system

    Li-Chiu Chang;Fi-John Chang

  • Evolutionary artificial neural networks for hydrological systems forecasting

    Yung-hsiang Chen;Yung-hsiang Chen;Fi-John Chang

  • Constrained genetic algorithms for optimizing multi-use reservoir operation

    Li-Chiu Chang;Fi-John Chang;Kuo-Wei Wang;Shin-Yi Dai

  • Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management

    Fi-John Chang;Li Chen

  • The strategy of building a flood forecast model by neuro‐fuzzy network

    Shen-Hsien Chen;Yong-Huang Lin;Li-Chiu Chang;Fi-John Chang

  • Real‐time recurrent learning neural network for stream‐flow forecasting

    F.-John Chang;Li-Chiu Chang;Hau-Lung Huang

  • Multi-step-ahead neural networks for flood forecasting

    Fi-John Chang;Yen-Ming Chiang;Li-Chiu Chang

  • Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting

    Yanlai Zhou;Fi-John Chang;Li-Chiu Chang;I-Feng Kao

  • Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts

    Yanlai Zhou;Yanlai Zhou;Yanlai Zhou;Shenglian Guo;Fi-John Chang

  • Reinforced recurrent neural networks for multi-step-ahead flood forecasts

    Pin-An Chen;Li-Chiu Chang;Fi-John Chang

  • Intelligent reservoir operation system based on evolving artificial neural networks

    Paulo Chaves;Fi-John Chang

  • Dynamic ANN for precipitation estimation and forecasting from radar observations

    Yen-Ming Chiang;Fi-John Chang;Ben Jong-Dao Jou;Pin-Fang Lin

Frequent Co-Authors

Li-Chiu Chang
Li-Chiu Chang Tamkang University
Shenglian Guo
Shenglian Guo Wuhan University
Chen-Wuing Liu
Chen-Wuing Liu National Taiwan University
Chong-Yu Xu
Chong-Yu Xu North China University of Water Conservancy and Electric Power
Chung-Min Liao
Chung-Min Liao National Taiwan University
Kuolin Hsu
Kuolin Hsu University of California, Irvine
Shuh-Ji Kao
Shuh-Ji Kao Hainan University
Amin Elshorbagy
Amin Elshorbagy University of Saskatchewan
Pan Liu
Pan Liu Shanghai Jiao Tong University
J.C. Huang
J.C. Huang City University of Hong Kong

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