Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Wen-Ching Wang ()
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Wen-Ching Wang: National Taitung University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2018, vol. 93, issue 2, No 18, 939 pages

Abstract: Abstract Disasters are difficult for humans to control but engineering is something that humans can plan and implement. The objective of slope control engineering is to use blocking, diversion, avoidance, or elimination of slopes to achieve control benefits such as reducing disaster damage and lowering the probability of disasters occurring. Control engineering benefit evaluation generally evaluates a single case. The subject of this study is Taitung County, of which 93.7% is covered in slopes. The data source for spatial clustering analysis and building of slope control effectiveness index done in this study came from the 484 disaster records and 1513 control engineering records that were recorded between 2001 and 2015. Spatial statistical analysis was conducted on data for the large spatial range so the following can be achieved: (1) define disaster control engineering effectiveness ratio Ri so that it can be used as the determination baseline appropriate for the regional control model; and (2) use the Kernel density estimate to produce slope disaster and control engineering cluster group space. Disaster control rate Pr was defined based on cluster characteristics to be used evaluation index for evaluating disaster control level. Engineering effectiveness rate Qr index was also defined to be used as the evaluation index for evaluating engineering effectiveness. The evaluation analysis results of the two indexes were used to explore the correlation between disasters and control engineering. The result of the disaster control engineering effectiveness ratio analysis uses Ri = 32% as the determination baseline for the disaster control model within the research sample area. Correlation analysis is shown based on the Pr and Qr index evaluation result. Because the disaster range in the sample space has expanded, engineering has difficulty immediately controlling all the disaster points, which caused poor performance in “disaster control rate Pr.” The performance of the “engineering effectiveness rate Qr” showed that regression relationship has a significant and positive correlation. This indicated that control engineering has a high centrality along with the occurrence of disasters and that engineering implementation can mitigate disasters.

Keywords: Slope disaster; Spatial cluster; Control engineering effectiveness ratio; Control model (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11069-018-3332-x

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