Literature DB >> 33893365

A probabilistic method for mapping earth fissure hazards.

Mingdong Zang1, Jianbing Peng2,3, Nengxiong Xu4, Zhijie Jia5.   

Abstract

Earth fissures caused by tectonic forces, human activities, or both seriously threaten the safety of people's lives and properties. The Taiyuan Basin, a Cenozoic downfaulted basin located in the centre of the Fen-Wei Basin tectonic belt, in northwestern China, presents the ideal study area for a hazard assessment of earth fissures. A total of 104 earth fissures have been observed in the Taiyuan Basin, with a total length of approximately 128 km. In this paper, we proposed a probabilistic method for mapping earth fissure hazards by integrating the analytic hierarchy process (AHP), the area under the curve (AUC), and the certainty factor model (CFM). Geomorphic units, geologic formations, active faults and land subsidence zones of the Taiyuan Basin were mapped in detail. Correlations between these factors and earth fissures were evaluated through spatial modelling in ArcGIS. The AUC was introduced into the AHP to weight each factor and thus, to derive an earth fissure susceptibility map. Finally, the modelled earth fissure susceptibility was compared with a digital inventory of earth fissures to develop a probability function and map the spatial variability in failure probability through the CFM. The study indicates that active faults have the greatest contribution to the generation of earth fissures. Earth fissures are prone to develop in the piedmont alluvial-diluvial clinoplain and the transitional zone near the geomorphic boundary. This mapping procedure can assist in making rational decisions regarding urban planning and infrastructure development in areas susceptible to earth fissures.

Entities:  

Year:  2021        PMID: 33893365     DOI: 10.1038/s41598-021-87995-1

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  3 in total

1.  Earth fissure hazard prediction using machine learning models.

Authors:  Bahram Choubin; Amir Mosavi; Esmail Heydari Alamdarloo; Farzaneh Sajedi Hosseini; Shahaboddin Shamshirband; Kazem Dashtekian; Pedram Ghamisi
Journal:  Environ Res       Date:  2019-09-23       Impact factor: 6.498

2.  Use and misuse of the receiver operating characteristic curve in risk prediction.

Authors:  Nancy R Cook
Journal:  Circulation       Date:  2007-02-20       Impact factor: 29.690

3.  Statistics review 13: receiver operating characteristic curves.

Authors:  Viv Bewick; Liz Cheek; Jonathan Ball
Journal:  Crit Care       Date:  2004-11-04       Impact factor: 9.097

  3 in total

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