World's Best Scientists 2026 revealed!
Li-Chiu Chang

Li-Chiu Chang

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
5888
World Ranking
6691
National Ranking
67

Li-Chiu Chang 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 Li-Chiu Chang 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: 79 publications — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Li-Chiu Chang 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 Li-Chiu Chang 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: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

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

Overview

Li-Chiu Chang is affiliated with Tamkang University in Taiwan and specializes in Environmental Science, with a substantial focus on Environmental Engineering, Global and Planetary Change, Water Science and Technology, Atmospheric Science, and Health, Toxicology and Mutagenesis. Their research centers around areas such as hydrological forecasting using artificial intelligence, flood risk assessment and management, hydrology and watershed management studies, air quality monitoring and forecasting, air quality and health impacts, atmospheric chemistry and aerosols, as well as tropical and extratropical cyclones research.

Chang's publication record includes contributions to several journals, with frequent publications appearing in the Journal of Hydrology, The Science of The Total Environment, Journal of Environmental Management, Water, and Journal of Cleaner Production. These venues highlight the scientist's engagement with topics related to water resources, environmental management, and air quality.

Recent papers by Li-Chiu Chang include:

  • Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting, 2020, 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
  • Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques, 2020, The Science of The Total Environment
  • An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies, 2020, Applied Energy
  • Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis, 2022, Journal of Hydrology

Collaboration plays a notable role in Chang's research, with frequent co-authors including Fi-John Chang, Pu-Yun Kow, Yanlai Zhou, Jia-Yi Liou, and Wei Sun. These collaborations suggest active participation in interdisciplinary and multi-author projects within their areas of expertise.

Main research topics covered by Li-Chiu Chang are:

  • Hydrological Forecasting Using AI
  • Flood Risk Assessment and Management
  • Hydrology and Watershed Management Studies
  • Air Quality Monitoring and Forecasting
  • Air Quality and Health Impacts
  • Atmospheric chemistry and aerosols
  • Tropical and Extratropical Cyclones Research

Chang's body of work reflects an integration of machine learning methods and environmental science applications, particularly examining hydrological and atmospheric phenomena. The scientist's research bridges technical innovation in forecasting models with practical concerns in water resource management and air quality, supporting ongoing studies in environmental engineering and global change dynamics.

Best Publications

  • 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

  • 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

  • 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

  • Constrained genetic algorithms for optimizing multi-use reservoir operation

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

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

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

  • Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

    Fi-John Chang;Li-Chiu Chang;Chien-Wei Huang;I-Feng Kao

  • 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

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

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

  • Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network

    Fi-John Chang;Li-Chiu Chang;Huey-Shan Kao;Gwo-Ru Wu

  • Guiding rational reservoir flood operation using penalty-type genetic algorithm

    Li-Chiu Chang

  • Clustering-based hybrid inundation model for forecasting flood inundation depths

    Li-Chiu Chang;Hung-Yu Shen;Yi-Fung Wang;Jing-Yu Huang

  • Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

    Ya-Ting Chang;Li-Chiu Chang;Fi-John Chang

  • Regional flood inundation nowcast using hybrid SOM and dynamic neural networks

    Li-Chiu Chang;Hung-Yu Shen;Fi-John Chang

  • A two-step-ahead recurrent neural network for stream-flow forecasting

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

  • Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting

    Pu-Yun Kow;Yi-Shin Wang;Yanlai Zhou;Yanlai Zhou;I-Feng Kao

  • AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

    Wen Ping Tsai;Fi John Chang;Li Chiu Chang;Edwin E. Herricks

Frequent Co-Authors

Fi-John Chang
Fi-John Chang National Taiwan University
Shenglian Guo
Shenglian Guo Wuhan University
Chong-Yu Xu
Chong-Yu Xu North China University of Water Conservancy and Electric Power
Ming-Jer Tsai
Ming-Jer Tsai Baylor College of Medicine

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