Cloud model-based evaluation of landslide dam development feasibility

As natural backwater structures, landslide dams both threaten downstream human settlement or infrastructure and contain abundant hydro-energy and tourism resources, so research on their development feasibility is of great significance for permanently remedying them and effectively turning disasters into benefits. Through an analysis of the factors influencing landslide dam development and utilization, an index system (consisting of target, rule, and index layers) for evaluating development feasibility was constructed in this paper. Considering uncertainty and randomness in development feasibility evaluation, a cloud model-improved evaluation method was proposed to determine membership and score clouds based on the uncertainty reasoning of cloud model, and a cloud model-improved analytic hierarchy process (AHP-Cloud Model) was introduced to obtain weights. Final evaluation results were obtained using a hierarchical weighted summary. The improved method was applied to evaluate the Hongshiyan and Tangjiashan landslide dams and the results were compared with the maximum membership principle results. The results showed that the cloud model depicted the fuzziness and uncertainty in the evaluation process. The improved method proposed in this paper overcame the loss of fuzziness in the maximum membership principle evaluation results, and was capable of more directly presenting evaluation results. The development feasibility of the Hongshiyan landslide dam was relatively high, while that of the Tangjiashan landslide dam was relatively low. As suggested by these results, the evaluation model proposed in this paper has great significance for preparing a long-term management scheme for landslide dams.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:16

Enthalten in:

PloS one - 16(2021), 5 vom: 12., Seite e0251212

Sprache:

Englisch

Beteiligte Personen:

Luo, Dengze [VerfasserIn]
Li, Hongtao [VerfasserIn]
Wu, Yu [VerfasserIn]
Li, Dong [VerfasserIn]
Yang, Xingguo [VerfasserIn]
Yao, Qiang [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 28.10.2021

Date Revised 28.10.2021

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.1371/journal.pone.0251212

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM325308004