关键词: 多粒度时空对象, 多尺度地理对象耦合演化, 地理过程, Bayes网络, 关联关系, 概率图模型, 要素特征状态, 新安江模型

Abstract:

Spatio-temporal objects of multi-granularity have the characteristics of multi-granularity, multi-type, multi-form, multi-reference system, multi-relation, multi-dimensional dynamics, and multi-energy autonomy. It can be used to directly describe the real world from micro to macro. Based on the spatio-temporal objects modeling theory, constructing the integrated expression of the coupled evolution of multi-scale geographic objects is the key to supporting geographic analysis and modeling with spatio-temporal objects of multi-granularity model. Based on spatio-temporal objects of multi-granularity modeling theory, this paper develops a Bayesian network-based geographic process evolution expression and modeling method on the basis of probability diagrams and conditional probability tables. This method uses spatio-temporal objects of multi-granularity as Bayesian network nodes, and constructs Bayesian network according to the association relationship between spatio-temporal objects of multi-granularity. It uses Bayesian probability to express the strength of the relationship between spatio-temporal objects of multi-granularity. And it describes the dynamic changes of the feature state of the elements through the update operator and the probability graph model. Based on this method, the Xin'anjiang Model is selected to conduct the modeling and simulation experiment of the geographic process of spatio-temporal objects of multi-granularity. This paper uses the hydrological data of Chengcun Village from 1989 to 1995 as training data, and the hydrological data of 1996 as simulated data. Using precipitation surface, evaporation surface, runoff surface and confluence surface to construct Bayesian network and simulate the state of runoff and sink flow. The experimental results show that the method can not only model the evolution of hydrological process, but also can simulate the changes of runoff and sink flow in the hydrological process, and the correct rate can reach 97.5% and 95.9%.

Key words: spatio-temporal objects of multi-granularity, the coupled evolution of multi-scale geographic objects, geographic process, bayesian network, connection relation, probabilistic graphical model, feature state of the elements, the Xin'an jiang Model