Modeling of Geographical Process Evolution of Spatio-temporal Objects of Multi-granularity based on Bayesian Network：A Case Study of the Xin'an Jiang Model

被引：0
|
作者：
Zhang Z. [1 ,2 ]
Yan Z. [1 ,2 ]
Wang Z. [1 ,2 ]
Fu R. [1 ,2 ]
Luo W. [1 ,2 ,3 ]
Yu Z. [1 ,2 ,3 ]
机构：
[1] Key Laboratory of Virtual Geographic Environment of The Ministry of Education (Nanjing Normal University), Nanjing
[2] Cultivation Base of State Key Laboratory of Geographical Environment Evolution, Nanjing
[3] Jiangsu Provincial Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
来源：
Journal of Geo-Information Science | 2021年 / 23卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Bayesian network; Connection relation; Feature state of; Geographic process; Probabilistic graphical model; Spatio-temporal objects of multi-granularity; The coupled evolution of multi-scale geographic objects; the elements; The Xin'an jiang Model;
D O I：
10.12082/dqxxkx.2021.200426
中图分类号：
学科分类号：
摘要：
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%. 2021, Science Press. All right reserved.
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页码：124 / 133
页数：9
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