Journal Article10.1007/S11053-018-9385-4
Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data
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TL;DR: 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 modelgenerated with discretely weighted exploration evidence data.
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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.
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Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble
TL;DR: In this article, two artificial intelligence (AI)-based models including adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza-Varas, Hargreaves-Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq.
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