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Landslide geological hazard susceptibility assessment is an important means of hazard prevention and reduction. The selection and optimization of susceptibility assessment model is very important. Sinan County was selected as the study area, and 16 assessment factors such as elevation, slope, curvature, lithology, land use, and average annual precipitation were selected. Frequency ratio (FR) model was coupled with support vector machine (SVM) model and random forest (RF) model. Grid search method was introduced to obtain the optimal parameter combination of SVM model, RF model and their coupling model for model training. Finally, SVM, RF, FR-SVM and FR-RF models were constructed to predict landslide susceptibility in the whole study area, and receiver operating characteristics (ROC) curve was performed verification. The results show that compared with the single machine learning model, the coupled machine learning model has more landslide hazard samples fall in the high zone and the very high zone, and has higher accuracy. In the single model, more landslide hazard samples in the RF model fall in the high zone and the extremely high zone. In the coupled model, more landslide hazard samples in the FR-RF model fall in the high zone and the very high zone, and no hazard samples points in the FR model and the FR-RF model fall in the very low zone, indicating that no matter the single model or the coupled model, The performance of RF model is better than that of SVM model. The AUC values of ROC prediction curves of the four models are 0.831 6, 0.843 9, 0.864 4 and 0.910 4, indicating that the coupling model combined with FR model and RF model has a higher accuracy, and this model is more suitable for the assessment of landslide susceptibility in Sinan County. The assessment results can provide some reference for hazard prevention and reduction of local landslide geological hazards.
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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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