Research of Forecasting Model for Regional Data in GIS Based on Back Propagation Neural Network

This paper focuses on space-time non-linear intelligent modeling for regional data, researches how to apply back-propagation neural network (BPN) into analysis of regional data. Thinking about sectional instability of spatial pattern, this paper divided space units of researching regions into different subregions by improved K-means algorithm based on spatial adjacency relationship. Then build a space-time model with BPN. To solve the problem that deviation is too large in determining boundary of zonings by BPN model when data dimension increased, this paper bring forward a modular BPN model which named regional space-time neural network (RSTNN) model, modeling and predicting respectively based on every zoning with BPN. At last, compare the abilities of modular BPN model and global BPN model by means of analysis of an example.