Tran Liem T, Knight C Gregory, O'Neill Robert V, Smith Elizabeth R, O'Connell Michael
Center for Integrated Regional Assessment, Department of Geography, The Pennsylvania State University, 2217 Earth and Engineering Sciences Building, University Park, PA 16802, USA.
Environ Manage. 2003 Jun;31(6):822-35. doi: 10.1007/s00267-003-2917-6.
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.
一种用于区域尺度环境评估的新方法已经被开发出来。这涉及到自组织映射（SOM）神经网络和主成分分析（PCA）的结合。该方法能够根据环境条件对生态系统进行聚类，并表明跨大区域多种因素的相对累积环境影响。利用大西洋中部地区的土地覆盖、人口、道路、溪流、空气污染和地形数据，该方法能够指出环境状况相对较差或易受未来恶化影响的区域。该方法结合了SOM和PCA的优势，提供了一种简单且有用的区域环境评估方法。与传统的聚类和排名方法相比，所描述的方法具有相当大的优势，例如为在多个尺度上可视化复杂的多维环境数据提供了一种有价值的手段，并且在促进进行更详细分析机会的同时，提供了区域环境评估所需的单一评估或排名。
