Susceptibility area regionalization of land subsidence based on extenics theory

• Published in: Cluster computing Vol. 20; no. 1; pp. 53 - 66
• Main Authors: Jia, Chao, Zhang, Yongwei, Han, Jingmin, Xu, Xiqiang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2017; Springer Nature B.V
• Subjects: Analytic hierarchy process; Classification; Computer Communication Networks; Computer Science; Correlation; Ecological effects; Energy conservation; Engineering; Evaluation; Geological hazards; Geology; Groundwater; High speed rail; Interdisciplinary subjects; Mining; Operating Systems; Processor Architectures; Subsidence; Urban environments; Water shortages; China; Land subsidence; Regionalization of susceptibility; Matter element model; Extenics; Analytic hierarchy process
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-016-0720-4

As a slowly changing geological hazard, land subsidence has been widely concerned by experts and scholars worldwide. xcessive extraction of groundwater is one of the main causes of land subsidence. Land subsidence has great adverse effects on high speed railway, urban environment, ecology, lifeline engineering, buildings and infrastructures. North China Plain is one of the important regions of land subsidence places in China. Dezhou city is a typical representative area. In this paper, an evaluating method is proposed to the land subsidence regionalization of susceptibility of Dezhou city in the North China Plain, based on matter element model deduced from the theories of extenics. Using the susceptible degree classification influencing factors of land subsidence, the elements in classical and sectorized field are defined, whereby an extension-evaluating model is developed to evaluate the comprehensive classification of susceptible degree of land subsidence by the correlation function of matter elements and extension sets. The analytic hierarchy process methodology for calculating weight aggregation of evaluating factors is proposed. The evaluating results are thus obtained through such an evaluation. An exemplification is shown that the present method is effective and applicable to forecast the classification of susceptible degree of land subsidence more accurately and scientifically, which lends the support for control planning for land subsidence and a more reliable basis for taking economic and reasonable supporting/reinforcing measures.