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Haoyuan Hong publication distribution in Earth Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Earth Science in 2026. The highlighted bar marks where Haoyuan Hong sits on this spectrum.

38–47 publications: 5 scientists 48–57 publications: 33 scientists 58–67 publications: 94 scientists 68–77 publications: 161 scientists 78–87 publications: 278 scientists 88–97 publications: 376 scientists 98–107 publications: 404 scientists 108–117 publications: 484 scientists 118–127 publications: 540 scientists 128–137 publications: 557 scientists 138–147 publications: 491 scientists 148–157 publications: 495 scientists 158–167 publications: 458 scientists 168–177 publications: 490 scientists 178–187 publications: 414 scientists 188–197 publications: 401 scientists 198–207 publications: 333 scientists 208–217 publications: 292 scientists 218–227 publications: 290 scientists 228–237 publications: 262 scientists 238–247 publications: 243 scientists 248–257 publications: 208 scientists 258–267 publications: 185 scientists 268–277 publications: 147 scientists 278–287 publications: 128 scientists 288–297 publications: 119 scientists 298–307 publications: 120 scientists 308–317 publications: 107 scientists 318–327 publications: 93 scientists 328–337 publications: 87 scientists 338–347 publications: 64 scientists 348–357 publications: 87 scientists 358–367 publications: 60 scientists 368–377 publications: 60 scientists 378–387 publications: 34 scientists 388–397 publications: 50 scientists 398–407 publications: 44 scientists 408–417 publications: 31 scientists 418–427 publications: 39 scientists 428–437 publications: 27 scientists 438–447 publications: 36 scientists 448–457 publications: 27 scientists 458–467 publications: 29 scientists 468–477 publications: 30 scientists 478–487 publications: 19 scientists 488–497 publications: 15 scientists 498–507 publications: 18 scientists 508–517 publications: 19 scientists 518–527 publications: 16 scientists 528–537 publications: 10 scientists 538–547 publications: 11 scientists 548–557 publications: 14 scientists 558–567 publications: 11 scientists 568–577 publications: 7 scientists 578–587 publications: 14 scientists 588–597 publications: 5 scientists 598–602 publications: 4 scientists 603+ publications: 100 scientists
38 publications 603+

The last bar groups every scientist with 603 publications or more.

Haoyuan Hong D-index placement in Earth Science in 2026

The chart shows the D-index (discipline H-index) distribution of Earth Science scientists ranked by Research.com in 2026. The highlighted bar marks where Haoyuan Hong sits on this spectrum.

30 D-Index: 144 scientists 31 D-Index: 215 scientists 32 D-Index: 278 scientists 33 D-Index: 379 scientists 34 D-Index: 366 scientists 35 D-Index: 372 scientists 36 D-Index: 389 scientists 37 D-Index: 366 scientists 38 D-Index: 344 scientists 39 D-Index: 349 scientists 40 D-Index: 296 scientists 41 D-Index: 289 scientists 42 D-Index: 277 scientists 43 D-Index: 279 scientists 44 D-Index: 248 scientists 45 D-Index: 257 scientists 46 D-Index: 212 scientists 47 D-Index: 202 scientists 48 D-Index: 207 scientists 49 D-Index: 204 scientists 50 D-Index: 183 scientists 51 D-Index: 178 scientists 52 D-Index: 182 scientists 53 D-Index: 178 scientists 54 D-Index: 163 scientists 55 D-Index: 117 scientists 56 D-Index: 163 scientists 57 D-Index: 120 scientists 58 D-Index: 113 scientists 59 D-Index: 136 scientists 60 D-Index: 135 scientists 61 D-Index: 96 scientists 62 D-Index: 103 scientists 63 D-Index: 83 scientists 64 D-Index: 103 scientists 65 D-Index: 83 scientists 66 D-Index: 89 scientists 67 D-Index: 92 scientists 68 D-Index: 89 scientists 69 D-Index: 78 scientists 70 D-Index: 75 scientists 71 D-Index: 53 scientists 72 D-Index: 62 scientists 73 D-Index: 54 scientists 74 D-Index: 35 scientists 75 D-Index: 52 scientists 76 D-Index: 50 scientists 77 D-Index: 32 scientists 78 D-Index: 37 scientists 79 D-Index: 20 scientists 80 D-Index: 33 scientists 81 D-Index: 36 scientists 82 D-Index: 34 scientists 83 D-Index: 34 scientists 84 D-Index: 24 scientists 85 D-Index: 19 scientists 86 D-Index: 18 scientists 87 D-Index: 26 scientists 88 D-Index: 30 scientists 89 D-Index: 17 scientists 90 D-Index: 21 scientists 91 D-Index: 19 scientists 92 D-Index: 15 scientists 93 D-Index: 14 scientists 94 D-Index: 20 scientists 95 D-Index: 9 scientists 96 D-Index: 10 scientists 97 D-Index: 15 scientists 98 D-Index: 13 scientists 99 D-Index: 3 scientists 100 D-Index: 13 scientists 101 D-Index: 3 scientists 102 D-Index: 9 scientists 103 D-Index: 4 scientists 104 D-Index: 6 scientists 105 D-Index: 6 scientists 106+ D-Index: 98 scientists
30 D-Index 106+

The last bar groups every scientist with 106 D-Index or more.

Overview

Haoyuan Hong is affiliated with Nanjing University of Information Science and Technology in China. Their research primarily focuses on Environmental Science, with a significant emphasis on Global and Planetary Change, Management, Monitoring, Policy and Law, Atmospheric Science, Safety, Risk, Reliability and Quality, and Water Science and Technology.

The main topics of their work include:

  • Landslides and related hazards
  • Flood Risk Assessment and Management
  • Cryospheric studies and observations
  • Geotechnical Engineering and Analysis
  • Hydrology and Drought Analysis
  • Hydrology and Watershed Management Studies
  • Tree Root and Stability Studies

Haoyuan Hong has contributed to several publications in prominent journals. Frequent publication venues include:

  • Journal of Environmental Management
  • Remote Sensing
  • Computers & Geosciences
  • Journal of Hydrology
  • The Science of The Total Environment

Recent papers authored or co-authored by Haoyuan Hong are:

  • "Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping" (2020, Computers & Geosciences)
  • "Predicting flood susceptibility using LSTM neural networks" (2020, Journal of Hydrology)
  • "Comparative study of landslide susceptibility mapping with different recurrent neural networks" (2020, Computers & Geosciences)
  • "A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping" (2020, International Journal of Geographical Information Systems)
  • "Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble" (2020, The Science of The Total Environment)

Their frequent co-authors are:

  • Yi Wang
  • Wei Chen
  • Zhice Fang
  • Faming Huang
  • Ling Peng

Best Publications

  • A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

    Wei Chen;Xiaoshen Xie;Jiale Wang;Biswajeet Pradhan;Biswajeet Pradhan

  • A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

    Khabat Khosravi;Himan Shahabi;Binh Thai Pham;Jan Adamowski

  • Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

    Haoyuan Hong;Haoyuan Hong;Junzhi Liu;Junzhi Liu;Dieu Tien Bui;Biswajeet Pradhan;Biswajeet Pradhan

  • Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

    Haoyuan Hong;Biswajeet Pradhan;Chong Xu;Dieu Tien Bui

  • Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China.

    Yi Wang;Zhice Fang;Haoyuan Hong;Haoyuan Hong

  • Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.

    Wei Chen;Yang Li;Weifeng Xue;Himan Shahabi

  • Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models

    Haoyuan Hong;Hamid Reza Pourghasemi;Zohre Sadat Pourtaghi

  • Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.

    Wei Chen;Jianbing Peng;Haoyuan Hong;Haoyuan Hong;Himan Shahabi

  • Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

    Haoyuan Hong;Paraskevas Tsangaratos;Ioanna Ilia;Junzhi Liu;Junzhi Liu

  • Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.

    Haoyuan Hong;Haoyuan Hong;Mahdi Panahi;Ataollah Shirzadi;Tianwu Ma;Tianwu Ma

  • Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping

    Yanli Wu;Yutian Ke;Zhuo Chen;Shouyun Liang

  • Predicting flood susceptibility using long short-term memory (LSTM) neural network model

    Unknown

  • Multi-phase distribution of organic micropollutants in Xiamen Harbour, China

    JL Zhou;H Hong;Z Zhang;K Maskaoui

  • Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India

    Mehebub Sahana;Haoyuan Hong;Haoyuan Hong;Haroon Sajjad

  • Flood susceptibility mapping using convolutional neural network frameworks

    Yi Wang;Zhice Fang;Haoyuan Hong;Ling Peng

  • Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles

    Wei Chen;Wei Chen;Haoyuan Hong;Haoyuan Hong;Shaojun Li;Himan Shahabi

  • GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

    Mahyat Shafapour Tehrany;Farzin Shabani;Mustafa Neamah Jebur;Haoyuan Hong

  • Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

    Zhice Fang;Yi Wang;Ling Peng;Haoyuan Hong

  • GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method

    Wei Chen;Xiaoshen Xie;Jianbing Peng;Himan Shahabi

  • A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping

    Zhice Fang;Yi Wang;Ling Peng;Haoyuan Hong

  • A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

    Wei Chen;Ataollah Shirzadi;Himan Shahabi;Baharin Bin Ahmad

  • Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy

    Haoyuan Hong;Wei Chen;Chong Xu;Ahmed M. Youssef

Frequent Co-Authors

A-Xing Zhu
A-Xing Zhu University of Wisconsin–Madison
Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Chong Xu
Chong Xu China Earthquake Administration
Himan Shahabi
Himan Shahabi University of Kurdistan
Ataollah Shirzadi
Ataollah Shirzadi University of Kurdistan
Baharin Bin Ahmad
Baharin Bin Ahmad University of Technology Malaysia
Hamid Reza Pourghasemi
Hamid Reza Pourghasemi Shiraz University
John L. Zhou
John L. Zhou University of Technology Sydney
Minhan Dai
Minhan Dai Xiamen University

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