Particle swarm optimization algorithm for neuro-fuzzy prospectivity analysis using continuously weighted spatial exploration data

Roshanravan, Bijan, Aghajani, Hamid, Yousefi, Mahyar, and Kreuzer, Oliver (2019) Particle swarm optimization algorithm for neuro-fuzzy prospectivity analysis using continuously weighted spatial exploration data. Natural Resources Research, 28 (2). pp. 309-325.

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Abstract

Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.

Item ID: 57730
Item Type: Article (Research - C1)
ISSN: 1573-8981
Keywords: Continuous weighting, Exploration targeting, Neuro-fuzzy, Particle swarm optimization algorithm
Copyright Information: © 2018 International Association for Mathematical Geosciences.
Date Deposited: 27 Mar 2019 07:31
FoR Codes: 37 EARTH SCIENCES > 3703 Geochemistry > 370301 Exploration geochemistry @ 35%
37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 35%
46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461305 Data structures and algorithms @ 30%
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