GEOGRAPHICAL RESEARCH ›› 2011, Vol. 30 ›› Issue (9): 1648-1659.doi: 10.11821/yj2011090009

• Earth Surface Processes • Previous Articles     Next Articles

Comprehensive physiographic regionalization of China using GIS and SOFM neural network

HUANG Jiao, GAO Yang, ZHAO Zhi-qiang, LI Shuang-cheng   

  1. College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Peking University, Beijing 100871, China
  • Received:2010-10-19 Revised:2011-04-28 Online:2011-09-20 Published:2011-09-20

Abstract: Comprehensive physiographic regionalization has long been a core issue of physical geography in China. A great number of regionalization themes have been developed and applied as guidelines for regional development and geography teaching. However, these themes mainly use the traditional expertise-experiences-based regionalization methodology, which probably make themselves unreliable due to certain prejudices and different knowledge backgrounds of each individual. In order to overcome this obstacle, and to enrich regionalization research theoretically and methodologically, this paper tries to apply SOFM neural network to the regionalization. Supported by GIS technology and following the traditional three-level-strategy, we construct and operate SOFM neural networks at each level, using temperature factors, moisture factors and supplement factors respectively. Finally, we divide Chinese mainland into 8 temperature zones, 17 moisture regions and 43 natural sub-regions, then compare this scheme with those based on traditional methods. The result shows that based on GIS platform, applying SOFM neural network into comprehensive physiographic regionalization has significant advantages, which is an important supplement and development to traditional regionalization paradigm.

Key words: comprehensive physiographic regionalization, GIS, SOFM neural network, China