Regional partitioning of agricultural non-point source pollution in China using a projection pursuit cluster model

Abstract

A projection pursuit cluster (PPC) model was used to analyze the regional partitioning of agricultural non-point source pollution in China. The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function. A novel optimization algorithm called Free search (FS) was introduced to optimize the projection direction of the PPC model. By making the appropriate improvements as we explored the use of the algorithm, it became simpler, and developed better exploration abilities. Thus, the multi-factor problem was converted into a single-factor cluster, according to the projection, which suc- cessfully avoided subjective disturbance and produced objective results. The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China, indicating that the PPC model is a powerful tool in multi-factor cluster analysis, and could be a new method for the regional partitioning of agricultural non-point source pollution.