Journal Article

Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data

Kreuzer, Oliver, Roshanravan, Bijan, Aghajani, Hamid, Yousefi, Mahyar

2019

Overview

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.

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Artificial intelligence / Artificial neural networks / Chemistry and Earth Sciences / Computer Science / Earth and Environmental Science / Earth Sciences / Efficiency / Exploration / Fault lines / Fossil Fuels (incl. Carbon Capture) / Fuzzy logic / Geography / Mathematical Modeling and Industrial Mathematics / Mineral exploration / Mineral Resources / Neural networks / Optimization algorithms / Original Paper / Particle swarm optimization / Physics / Spatial data / Statistics for Engineering / Sustainable Development