World's Best Scientists 2026 revealed!
Emmanuel John M. Carranza

Emmanuel John M. Carranza

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Earth Science
South Africa
2026

D-Index & Metrics

Earth Science

D-Index
82
Citations
21314
World Ranking
473
National Ranking
1

Emmanuel John M. Carranza 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 Emmanuel John M. Carranza sits on this spectrum.

38–42 publications: 1 scientists 43–47 publications: 0 scientists 48–52 publications: 3 scientists 53–57 publications: 8 scientists 58–62 publications: 11 scientists 63–67 publications: 16 scientists 68–72 publications: 24 scientists 73–77 publications: 31 scientists 78–82 publications: 49 scientists 83–87 publications: 77 scientists 88–92 publications: 86 scientists 93–97 publications: 88 scientists 98–102 publications: 92 scientists 103–107 publications: 84 scientists 108–112 publications: 102 scientists 113–117 publications: 108 scientists 118–122 publications: 117 scientists 123–127 publications: 126 scientists 128–132 publications: 146 scientists 133–137 publications: 114 scientists 138–142 publications: 102 scientists 143–147 publications: 127 scientists 148–152 publications: 102 scientists 153–157 publications: 112 scientists 158–162 publications: 102 scientists 163–167 publications: 127 scientists 168–172 publications: 121 scientists 173–177 publications: 112 scientists 178–182 publications: 109 scientists 183–187 publications: 98 scientists 188–192 publications: 92 scientists 193–197 publications: 105 scientists 198–202 publications: 77 scientists 203–207 publications: 80 scientists 208–212 publications: 89 scientists 213–217 publications: 70 scientists 218–222 publications: 74 scientists 223–227 publications: 74 scientists 228–232 publications: 70 scientists 233–237 publications: 67 scientists 238–242 publications: 59 scientists 243–247 publications: 67 scientists 248–252 publications: 51 scientists 253–257 publications: 46 scientists 258–262 publications: 41 scientists 263–267 publications: 39 scientists 268–272 publications: 34 scientists 273–277 publications: 39 scientists 278–282 publications: 35 scientists 283–287 publications: 36 scientists 288–292 publications: 35 scientists 293–297 publications: 29 scientists 298–302 publications: 23 scientists 303–307 publications: 39 scientists 308–312 publications: 34 scientists 313–317 publications: 23 scientists 318–322 publications: 18 scientists 323–327 publications: 18 scientists 328–332 publications: 21 scientists 333–337 publications: 20 scientists 338–342 publications: 15 scientists 343–347 publications: 13 scientists 348–352 publications: 24 scientists 353–357 publications: 25 scientists 358–362 publications: 10 scientists 363–367 publications: 20 scientists 368–372 publications: 17 scientists 373–377 publications: 18 scientists 378–382 publications: 15 scientists 383–387 publications: 7 scientists 388–392 publications: 22 scientists 393–397 publications: 9 scientists 398–402 publications: 11 scientists 403–407 publications: 16 scientists 408–412 publications: 4 scientists 413–417 publications: 5 scientists 418–422 publications: 14 scientists 423–427 publications: 8 scientists 428–432 publications: 7 scientists 433–437 publications: 7 scientists 438–442 publications: 10 scientists 443–447 publications: 10 scientists 448–452 publications: 3 scientists 453–457 publications: 9 scientists 458–462 publications: 11 scientists 463–467 publications: 4 scientists 468–472 publications: 6 scientists 473–477 publications: 11 scientists 478–482 publications: 8 scientists 483–487 publications: 2 scientists 488–492 publications: 3 scientists 493–497 publications: 4 scientists 498–502 publications: 4 scientists 503–507 publications: 4 scientists 508–509 publications: 4 scientists 510+ publications: 100 scientists
38 publications 510+

This scientist: 308 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 510 publications or more.

Emmanuel John M. Carranza 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 Emmanuel John M. Carranza sits on this spectrum.

30 D-Index: 15 scientists 31 D-Index: 42 scientists 32 D-Index: 60 scientists 33 D-Index: 90 scientists 34 D-Index: 103 scientists 35 D-Index: 108 scientists 36 D-Index: 142 scientists 37 D-Index: 147 scientists 38 D-Index: 147 scientists 39 D-Index: 152 scientists 40 D-Index: 143 scientists 41 D-Index: 157 scientists 42 D-Index: 141 scientists 43 D-Index: 151 scientists 44 D-Index: 140 scientists 45 D-Index: 135 scientists 46 D-Index: 121 scientists 47 D-Index: 108 scientists 48 D-Index: 128 scientists 49 D-Index: 111 scientists 50 D-Index: 106 scientists 51 D-Index: 112 scientists 52 D-Index: 102 scientists 53 D-Index: 103 scientists 54 D-Index: 94 scientists 55 D-Index: 61 scientists 56 D-Index: 101 scientists 57 D-Index: 71 scientists 58 D-Index: 65 scientists 59 D-Index: 78 scientists 60 D-Index: 93 scientists 61 D-Index: 56 scientists 62 D-Index: 58 scientists 63 D-Index: 52 scientists 64 D-Index: 63 scientists 65 D-Index: 49 scientists 66 D-Index: 53 scientists 67 D-Index: 57 scientists 68 D-Index: 50 scientists 69 D-Index: 44 scientists 70 D-Index: 44 scientists 71 D-Index: 31 scientists 72 D-Index: 33 scientists 73 D-Index: 33 scientists 74 D-Index: 23 scientists 75 D-Index: 32 scientists 76 D-Index: 24 scientists 77 D-Index: 17 scientists 78 D-Index: 19 scientists 79 D-Index: 9 scientists 80 D-Index: 21 scientists 81 D-Index: 20 scientists 82 D-Index: 17 scientists 83 D-Index: 19 scientists 84 D-Index: 19 scientists 85 D-Index: 14 scientists 86 D-Index: 6 scientists 87 D-Index: 14 scientists 88 D-Index: 17 scientists 89 D-Index: 10 scientists 90 D-Index: 8 scientists 91 D-Index: 8 scientists 92 D-Index: 9 scientists 93 D-Index: 6 scientists 94+ D-Index: 98 scientists
30 D-Index 94+

This scientist: 82 D-Index — 95th percentile

95% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 94 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Earth Science in South Africa Leader Award
  • 2025 - Research.com Earth Science in South Africa Leader Award
  • 2022 - Research.com Earth Science in South Africa Leader Award

Overview

Emmanuel John M. Carranza is affiliated with the University of the Free State in South Africa. The scientist's research primarily spans interdisciplinary fields, including Computer Science, Engineering, and Earth and Planetary Sciences. Their subfields of study encompass Artificial Intelligence, Geophysics, Media Technology, Mechanical Engineering, and Environmental Engineering.

The main topics of Emmanuel John M. Carranza's research include:

  • Geochemistry and Geologic Mapping
  • Geological and Geochemical Analysis
  • Remote-Sensing Image Classification
  • Mineral Processing and Grinding
  • Soil Geostatistics and Mapping
  • Hydrocarbon exploration and reservoir analysis
  • Earthquake and tectonic studies

Frequent collaborators in their work are Jiajun Liu, Gongwen Wang, Shuyun Xie, Degao Zhai, and Shuai Zhang, reflecting repeated partnerships in numerous publications.

Emmanuel John M. Carranza has published extensively across several scientific journals. The main venues where their research appears include:

  • Natural Resources Research
  • Ore Geology Reviews
  • Minerals
  • Journal of Geochemical Exploration
  • Mathematical Geosciences

Recent papers illustrate their focus on data-driven methods and mineral prospectivity modeling. Notable publications include:

  • Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook (2021, Journal of Geochemical Exploration)
  • Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network (2021, Natural Resources Research)
  • Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data (2020, Ore Geology Reviews)
  • Regional-Scale Mineral Prospectivity Mapping: Support Vector Machines and an Improved Data-Driven Multi-criteria Decision-Making Technique (2021, Natural Resources Research)
  • Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity (2021, Natural Resources Research)

Best Publications

  • Geochemical Anomaly and Mineral Prospectivity Mapping in Gis

    Emmanuel John M. Carranza

  • Arsenic geochemistry and health

    Alfred A Duker;E J M Carranza;Martin Hale

  • Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

    Jamshid Farifteh;F Van der Meer;C Atzberger;E. J. M Carranza

  • Support vector machine: A tool for mapping mineral prospectivity

    Renguang Zuo;Emmanuel John M. Carranza

  • Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values

    Emmanuel John M. Carranza

  • Deep learning and its application in geochemical mapping

    Renguang Zuo;Yihui Xiong;Jian Wang;Emmanuel John M. Carranza

  • Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

    Emmanuel John M. Carranza;Alice G. Laborte

  • Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling

    Mahyar Yousefi;Emmanuel John M. Carranza

  • Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features

    Emmanuel John M. Carranza

  • Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India

    Alok Porwal;E. J. M. Carranza;M. Hale;M. Hale

  • Geochemical mineralization probability index GMPI : a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping

    Mahyar Yousefi;Abolghasem Kamkar-Rouhani;Emmanuel John M. Carranza

  • Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

    Alok Porwal;E. J. M. Carranza;M. Hale;M. Hale

  • Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping

    E.J.M. Carranza;M. Hale;M. Hale;C. Faassen

  • Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm

    Emmanuel John M. Carranza;Alice G. Laborte

  • Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

    Mahyar Yousefi;Emmanuel John M. Carranza

  • Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines

    Emmanuel John M. Carranza;Martin Hale

  • Cretaceous–Cenozoic tectonic history of the Jiaojia Fault and gold mineralization in the Jiaodong Peninsula, China: constraints from zircon U–Pb, illite K–Ar, and apatite fission track thermochronometry

    Jun Deng;Changming Wang;Changming Wang;Leon Bagas;Emmanuel John M. Carranza

  • Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential, Baguio District, Philippines

    Emmanuel John M. Carranza;Martin Hale

  • Fuzzy modeling of farmers' knowledge for land suitability classification

    Rodrigo S. Sicat;Emmanuel John M. Carranza;Uday Bhaskar Nidumolu

  • Multi - and hyperspectral geologic remote sensing : a review

    F.D. van der Meer;H.M.A. van der Werff;F.J.A. van Ruitenbeek;C.A. Hecker

  • Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines

    Emmanuel John M. Carranza

  • Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites, Lundazi District, Zambia

    E. J. M. Carranza;T. Woldai;E. M. Chikambwe

Frequent Co-Authors

Jiajun Liu
Jiajun Liu Chinese Academy of Geological Sciences
Jun Deng
Jun Deng China University of Geosciences
Renguang Zuo
Renguang Zuo China University of Geosciences
Abbas Maghsoudi
Abbas Maghsoudi Amirkabir University of Technology
Freek D. van der Meer
Freek D. van der Meer University of Twente
Qingfei Wang
Qingfei Wang China University of Geosciences
Victor Jetten
Victor Jetten University of Twente
Alfred Stein
Alfred Stein University of Twente
Qiuming Cheng
Qiuming Cheng China University of Geosciences
M. Santosh
M. Santosh China University of Geosciences

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