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Zhong-kai Feng

Zhong-kai Feng

Hohai University
China

D-Index & Metrics

Engineering and Technology

D-Index
38
Citations
5046
World Ranking
8138
National Ranking
1436

Zhong-kai Feng 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 Zhong-kai Feng 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: 100 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Zhong-kai Feng 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 Zhong-kai Feng 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: 38 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Zhong-kai Feng is affiliated with Hohai University in China. Their research primarily focuses on engineering and environmental science, with specific attention to subfields including electrical and electronic engineering, environmental engineering, ocean engineering, water science and technology, and artificial intelligence.

The scientist has published extensively on topics related to hydrological forecasting using artificial intelligence, energy load and power forecasting, water resources management and optimization, hydrology and watershed management studies, electric power system optimization, water systems and optimization, and water-energy-food nexus studies.

Among the recent notable publications are:

  • Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization (2020, Journal of Hydrology)
  • Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems (2020, Applied Soft Computing)
  • Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction (2021, Journal of Hydrology)
  • Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management (2020, Sustainable Cities and Society)
  • Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition (2020, Journal of Hydrologic Engineering)

Zhong-kai Feng has frequently published in several key venues including:

  • Journal of Hydrology
  • Applied Soft Computing
  • Water Resources Management
  • Journal of Hydrologic Engineering
  • Environmental Research Letters

Frequent collaborators in their research include:

  • Wen-jing Niu
  • Shuai Liu
  • Yinshan Xu
  • Wenchuan Wang
  • Zhiqiang Jiang

In addition to journal publications, Zhong-kai Feng has contributed to book literature, with works published by Frontiers Media, including the 2024 book titled "Key technologies for hybrid energy system planning and operation."

Best Publications

  • A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

    Feifei He;Jianzhong Zhou;Zhong-kai Feng;Guangbiao Liu

  • Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization

    Zhong-kai Feng;Wen-jing Niu;Zheng-yang Tang;Zhi-qiang Jiang

  • Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model

    Yongqi Liu;Hui Qin;Zhendong Zhang;Shaoqian Pei

  • Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems

    Zhong-kai Feng;Wen-jing Niu;Shuai Liu

  • Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization

    Zhong-kai Feng;Wen-jing Niu;Rui Zhang;Sen Wang

  • A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy

    Zhong-kai Feng;Wen-jing Niu;Wen-chuan Wang;Jian-zhong Zhou

  • Hydropower system operation optimization by discrete differential dynamic programming based on orthogonal experiment design

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng;Sheng-li Liao

  • China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng

  • Multi-stage progressive optimality algorithm and its application in energy storage operation chart optimization of cascade reservoirs

    Zhiqiang Jiang;Changming Ji;Hui Qin;Zhongkai Feng

  • Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm

    Wen-jing Niu;Zhong-kai Feng;Ming Zeng;Bao-fei Feng

  • Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation

    Zhendong Zhang;Hui Qin;Yongqi Liu;Liqiang Yao

  • A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China

    Wen-jing Niu;Zhong-kai Feng;Chun-tian Cheng;Xin-yu Wu

  • Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng

  • Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng

  • Forecasting Daily Runoff by Extreme Learning Machine Based on Quantum-Behaved Particle Swarm Optimization

    Wen-jing Niu;Zhong-kai Feng;Chun-tian Cheng;Jian-zhong Zhou

  • Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition

    Wen-jing Niu;Zhong-kai Feng;Yu-bin Chen;Hai-rong Zhang

  • Optimization of hydropower system operation by uniform dynamic programming for dimensionality reduction

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng;Xin-yu Wu

  • Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng;Jian-zhong Zhou

  • Optimizing electrical power production of hydropower system by uniform progressive optimality algorithm based on two-stage search mechanism and uniform design

    Zhong-kai Feng;Wen-jing Niu;Chun-tian Cheng

  • Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

    Zhong-Kai Feng;Wen-Jing Niu;Jian-Zhong Zhou;Chun-Tian Cheng

  • Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction

    Zhong-kai Feng;Wen-jing Niu;Zheng-yang Tang;Yang Xu

Frequent Co-Authors

Chuntian Cheng
Chuntian Cheng Dalian University of Technology
Jianzhong Zhou
Jianzhong Zhou Huazhong University of Science and Technology
Jay R. Lund
Jay R. Lund University of California, Davis

External Links

Personal website of Zhong-kai Feng

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