E
Emmanuel John M. Carranza
Researcher at University of KwaZulu-Natal
Publications - 290
Citations - 13630
Emmanuel John M. Carranza is an academic researcher from University of KwaZulu-Natal. The author has contributed to research in topics: Prospectivity mapping & Geology. The author has an hindex of 59, co-authored 262 publications receiving 10898 citations. Previous affiliations of Emmanuel John M. Carranza include State University of Campinas & University of Twente.
Papers
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Multi - and hyperspectral geologic remote sensing : a review
F.D. van der Meer,H.M.A. van der Werff,F.J.A. van Ruitenbeek,Christoph Hecker,Wim Bakker,M. Noomen,M. van der Meijde,Emmanuel John M. Carranza,J.B. de Smeth,Tsehaie Woldai +9 more
TL;DR: A review of multispectral and hyperspectral remote sensing data, products and applications in geology can be found in this paper, where the authors provide an overview of the main threats for geologic remote sensing lies in the lack of data continuity.
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Arsenic geochemistry and health
TL;DR: The impact of arsenic on the immune system is outlined, whose alteration could lead to viral/bacterial infections and possible controls are outlined.
Book
Geochemical Anomaly and Mineral Prospectivity Mapping in Gis
TL;DR: In this article, the authors present a survey of geochemical anomaly and mineral prospectivity mapping by using a geographic information system (GIS), and demonstrate the use of GIS-aided and GISbased techniques for spatial data analysis and geo-information sybhesis for conceptual and predictive modeling of mineral prospectivities.
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Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)
TL;DR: In this article, partial least squares regression (PLSR) and artificial neural network (ANN) were compared in order to examine linear and non-linear relationship between soil reflectance and salt concentration.
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Support vector machine: A tool for mapping mineral prospectivity
TL;DR: The results indicate the usefulness of SVM as a tool for predictive mapping of mineral prospectivity and the prospective target areas predicted by both SVM and WofE reflect the controls of Au deposit occurrence in the study area by NE-SW trending anticlines and contact zones.