Insights into the implementation of synoptic weather-type classification using self-organizing maps: an Australian case study

2014 ◽  
Vol 35 (12) ◽  
pp. 3471-3485 ◽  
Author(s):  
Ningbo Jiang ◽  
Kehui Luo ◽  
Paul J. Beggs ◽  
Kevin Cheung ◽  
Yvonne Scorgie
Keyword(s):  
Weather Type ◽  
Self Organizing Maps ◽  
Synoptic Weather ◽  
Australian Case ◽  
Type Classification ◽  
Self Organizing

Predictive Geological Mapping from Geophysical Data Using Self-Organizing Maps: A Case Study from Baie Verte, NL, Canada

Geophysics ◽  
2021 ◽  
pp. 1-55
Author(s):  
Angela Carter-McAuslan ◽  
Colin Farquharson
Keyword(s):  
Gravity Data ◽  
Geological Mapping ◽  
Magnetic Data ◽  
Self Organizing Maps ◽  
Predictive Mapping ◽  
Legacy Data ◽  
Geological Units ◽  
Radiometric Data ◽  
Self Organizing

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multi-variate datasets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We applied SOMs to magnetic, radiometric and gravity datasets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geological maps available for this area and the knowledge from numerous geological studies allowed for assessing the accuracy of the SOM-based predictive mapping. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geological units identified by previous traditional geological mapping. Of the combinations of datasets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the datasets. The SOM results could be used as input into k-means clustering as k-means clustering requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geological mapping would be most beneficial which can be especially useful in parts of the world where access is difficult and expensive.

scholarly journals Projected changes in synoptic weather patterns over New Zealand examined through self-organizing maps

2016 ◽  
Vol 36 (12) ◽  
pp. 3934-3948 ◽  
Author(s):  
Peter B. Gibson ◽  
Sarah E. Perkins-Kirkpatrick ◽  
James A. Renwick
Keyword(s):  
New Zealand ◽  
Self Organizing Maps ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Projected Changes ◽  
Self Organizing

Competition among European Tourist Cities: A Comparative Analysis with Multidimensional Scaling and Self-Organizing Maps

1995 ◽  
Vol 1 (3) ◽  
pp. 283-302 ◽  
Author(s):  
Josef A. Mazanec
Keyword(s):  
Comparative Analysis ◽  
Multidimensional Scaling ◽  
Profile Data ◽  
Self Organizing Maps ◽  
Tourist Destinations ◽  
European Cities ◽  
Som Network ◽  
Self Organizing

The tourism researcher's attention is drawn to the neurocomputing methodology of self-organizing maps (SOMs) SOMs can be used to find a radically parsimonious representation of multidimensional profile data. The properties of an SOM network are explored in a case study on the competitive relationships between European cities as tourist destinations. SOM results are compared with a conventional INDSCAL model. The database consists of the guest mix profiles of 26 major tourist cities between 1975 and 1992.

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