Regional Clustering for Ecological Geographical Parameters Based on SOFM Model

Aerial differentiation research is one of the important symbols by which scientists recognize the geographic phenomenon, mechanism and processes correctly. So, the demarcation of district boundaries has become an urgent and significant work, especially in southwest China with characteristics of varied atmospheric circulations and complex landforms. Clustering analysis based on self-organizing feature mapping (SOFM) network is a new unsupervised clustering method that develops from neural networks. In this paper, a neural network has been trained to perform complex functions in various fields of application, including elevation, temperature, precipitation, wind speed, active accumulated temperature, evapotranspiration potential and enhanced vegetation index (EVI) at 30 meteorological stations of Yunnan Province in China. The result reveals that Ailao Mountain is such a firm barrier blocking the cold air coming from northern into the southwest mountain region, and such a possible boundary between summer southwestern monsoon and winter northeastern monsoon in China, that it becomes a demarcation boundary of climate sort between west and east regions. This way, SOFM network is used in the aerial differentiation study of ecological geography, and is a rather good comprehensive physical regionalization method, for, it can reflect the similarities and differences of different areas near the basic boundaries, and reveals a continuous process from quantitative changes to qualitative changes.