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

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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