MATEC Web Conf.
Volume 175, 20182018 International Forum on Construction, Aviation and Environmental Engineering-Internet of Things (IFCAE-IOT 2018)
|Number of page(s)||7|
|Section||Urban Planning, Environment and Construction|
|Published online||02 July 2018|
Risk assessment of debris flow based on group decision making – A case study of Luanchuan County, Henan Province, China
School of Geography & Resource Science, Neijiang Normal University, 641100 Neijiang, China
2 Faculty of Engineering, China University of Geosciences, 430074 Wuhan, China
a Corresponding author: firstname.lastname@example.org
Luanchuan County, located in the mountains of Western Henan Province, is characterized by poor geological environment and abundant material sources and rainfalls. Debris flows have occurred many times in this county, and in some gully debris flows exhibit a large scale, requiring risk assessment. In the multi-factor comprehensive assessment methods for debris flow risk, it is really important to determine the weight of each factor since this affects the reliability of the assessment results. Given that the subjective weighting method can accurately reflect the importance of each factor, in order to improve the reliability of subjective weighting, the group decision making method is used to determine the weight of each factor. Group decision making is realized using the analytic hierarchy process and the data fusion algorithm. In this method, the expert combination weight is determined; on this basis, a model for comprehensive assessment of debris flow risk is established by the linear weighted sum method, and risk assessment is performed for gullies with medium to large-scale debris flows in the study area. The assessment results show that all debris flow gullies face minor to moderate risks. For gullies with high risk degree, it is suggested to timely clear material sources in channels and construct or reinforce retaining dams in order to prevent re-occurrence of debris flows.
© The Authors, published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.