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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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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