Risk assessment of submarine landslide based on spectral clustering

Du Xing; Sun Yongfu; Song Yupeng; Xiu Zongxiang; Shan Zhigang

doi:10.12284/hyxb2021023

Volume 43 Issue 1

Feb. 2021

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Du Xing，Sun Yongfu，Song Yupeng, et al. Risk assessment of submarine landslide based on spectral clustering[J]. Haiyang Xuebao，2021, 43(1)：93–101 doi: 10.12284/hyxb2021023

Citation:

Du Xing，Sun Yongfu，Song Yupeng, et al. Risk assessment of submarine landslide based on spectral clustering[J]. Haiyang Xuebao，2021, 43(1)：93–101 doi: 10.12284/hyxb2021023

Citation:

Du Xing，Sun Yongfu，Song Yupeng, et al. Risk assessment of submarine landslide based on spectral clustering[J]. Haiyang Xuebao，2021, 43(1)：93–101 doi: 10.12284/hyxb2021023

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Risk assessment of submarine landslide based on spectral clustering

doi: 10.12284/hyxb2021023

Du Xing^{1, 2
,},
Sun Yongfu^{3, 4},
Song Yupeng^{2, 3},
Xiu Zongxiang^{2, 3},
Shan Zhigang^{1
,
,}

1.
POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311122, China
2.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
3.
Laboratory for Marine Geology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
4.
National Deep Sea Center, Qingdao 266237, China

Received Date: 2019-12-11
Rev Recd Date: 2020-02-23

Available Online: 2021-02-24

Publish Date: 2021-01-25

Abstract

Abstract

The risk assessment and zoning of submarine landslides can guide the site selection and risk prevention of offshore engineering facilities. In this paper, an unsupervised machine learning spectral analysis algorithm was used to evaluate the risk of submarine landslides in the Chengdao sea area of the Yellow River Estuary. A model of submarine landslides risk assessment with 9 input parameters, 4 output parameters and 0.08 as kernel function parameters is constructed. By using this model, the study area can be divided into 4 parts: high, quite high, quite low and low risk of submarine landslide. The comparison between the evaluation results and the distribution characteristics of geological environment factors show that the most important factors are the type of seafloor sediment and hydrodynamic action, and the most important trigger factor is liquefaction. The analysis results of model parameters present that the evaluation results with slightly lower accuracy can be obtained by reasonably simplifying the input factors, and the kernel function parameter is important index affecting the evaluation accuracy. The above research shows that the unsupervised machine learning algorithm can be well used in the risk assessment of submarine landslides, and the richness and accuracy of data categories are important factors affecting the assessment accuracy.
- submarine landslide,
- Yellow River Estuary,
- risk assessment,
- unsupervised machine learning,
- spectral clustering

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References(22)

References

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