Dataset Information

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.

ABSTRACT: Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth's surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth's surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~?88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.

SUBMITTER: Arca D

PROVIDER: S-EPMC6244970 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.

Arca Deniz D Kutoğlu Hakan Ş HŞ Becek Kazimierz K

Environmental monitoring and assessment 20181114 12

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth's surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth's surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, in ...[more]

PMID: 30430322

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Project description:The aim of this study is to provide a landslide susceptibility map of the Xunyang District of a mountainous terrain, at the southern part of the Qin-Ba Mountain Region, which has been highly exposed to widely distributed shallow landslides over the past few decades. The Weight of Evidence (WoE) method was adopted in this research considering both the presence of a certain landslide causative factor class and the absence of remaining classes, which was used for determining a clearly spatial correlation between a landslide occurrence and the causative factors. Intrinsic factors, including geomorphological factors, geological factors, and river flow networks, and external factors of anthropogenic engineering activities in terms of density of road network were all considered and involved in the Geological Information System (GIS) environment for reconstructing the thematic layers of factor dataset. Significant assumptions prior to the analysis were emphasized to ensure conditional independence between each pair of factors for this bivariate statistical approach. In addition, a detailed landslide inventory map was constructed through field investigation and a remote sensing interpretation process at a scale of 1:50000. The thematic layers and landslide map were overlapped to obtain a spatial statistical relationship by using the frequency ratio method. At last, the validation process for the derived susceptibility map was conducted by applying the ROC curve, indicating that more than 90% of the landslides were in categories of high and moderate susceptibility zones. The causative factor classes, including the slope angles ranging from 20 to 40°, strong weathered and fractured strata, and road network density were identified to considerably influence the landslide distribution in the study area. The results have proven to be significantly meaningful for landslide hazard risk mitigation and land use management for the local authorities responsible for these fields.

Project description:ObjectiveMulticriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain.MethodsA bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords.ResultsA total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research.ConclusionThis study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Dataset Information

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.

Publications

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.

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