Paper Info
Title
Application of SOM to analysis of Minnesota soil survey data
Abstract
This paper describes data-analytic modeling of the Minnesota soil chemical data produced by the 2001 metro soil survey. The chemical composition of the soil is characterized by the concentration of many metal and non-metal constituents, resulting in high-dimensional data. This high dimensionality and possible unknown (nonlinear) correlations in the data make it difficult to analyze and interpret using standard statistical techniques. This paper applies Self Organizing Map (SOM), to present the high-dimensional soil data in a 2D format suitable for human understanding and interpretation. This SOM representation enables analysis of the soil chemical concentration trends within the Twin Cities Metropolitan area of Minnesota. These trends are important for various Minnesota regulatory agencies concerned with the concentration of polluting chemical elements due to human activities.
Year
DOI
Venue
2011
10.1109/IJCNN.2011.6033280
Neural Networks
Keywords
Field
DocType
data analysis,geology,self-organising feature maps,soil,statistical analysis,Minnesota soil chemical data,SOM,chemical composition,data-analytic modeling,self organizing map,soil chemical concentration,twin cities metropolitan area,Self-organizing maps (SOM),cluster analysis,geological surveying,pollution,soil chemical survey data
Soil science,Computer science,Soil survey,Pollution,Twin cities,Self-organizing map,Soil map,Artificial intelligence,Metropolitan area,Digital soil mapping,Machine learning,Statistical analysis
Conference
ISSN
ISBN
Citations 
2161-4393
978-1-4244-9635-8
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Sauptik Dhar1485.75
Vladimir Cherkassky21064126.66