Self-organizing maps for integrated environmental assessment of the Mid-Atlantic region.
A new method has been developed to perform environmental assessment at regional scale. This involves a combination of a self-organizing map (SOM) neural network and principal component analysis (PCA). The method is capable of clustering ecosystems in terms of environmental conditions and suggesting relative cumulative environmental impacts of multiple factors across a large region. Using data on land-cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic region, the method was able to indicate areas that are in relatively poor environmental condition or vulnerable to future deterioration. Combining the strengths of SOM with those of PCA, the method offers an easy and useful way to perform a regional environmental assessment. Compared with traditional clustering and ranking approaches, the described method has considerable advantages, such as providing a valuable means for visualizing complex multidimensional environmental data at multiple scales and offering a single assessment or ranking needed for a regional environmental assessment while still facilitating the opportunity for more detailed analyses.
Tran
Liem T
LT
Center for Integrated Regional Assessment, Department of Geography, The Pennsylvania State University, 2217 Earth and Engineering Sciences Building, University Park, PA 16802, USA. LTT1@psu.edu
Knight
C Gregory
CG
O'Neill
Robert V
RV
Smith
Elizabeth R
ER
O'Connell
Michael
M
D004784:Environmental Monitoring / Q000379:methods*; D040362:Geographic Information Systems; D016571:Neural Networks, Computer; D018570:Risk Assessment; D014481:United States