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Outline
To effectively predict slope stability and prevent slope instability occurrence, a hybrid model WOA-RF, combining whale optimization algorithm (WOA) and random forest (RF) was proposed. Based on the collected slope cases, the classification and generalization performance of the model was evaluated according to the classification performance indicators given by the confusion matrix and the area under the receiver operating characteristic curve. Additionally, WOA was used to optimize four widely used machine learning models, and the optimized machine learning models were compared with WOA-RF. The results demonstrate that WOA is effective in optimizing hyperparameters and improving model performance. The optimal WOA-RF model achieves an accuracy of 0.99 on training set and of 0.94 on test set. After optimization, the accuracy, the precision, the recall, and the hamonic mean of the precision and recall are increased by 11.9%, 19.0%, 4.8%, and 11.9%, respectively. Comparative analysis reveals that the WOA-RF model is superior to the others in all indicators. Furthermore, the feature importance ranking was determined. Analysis of the feature importance indicates that unit weight is the most sensitive feature affecting slope stability. The established WOA-RF model is proved effective in predicting slope stability and facilitating the development of appropriate protective measures based on the predicted results.
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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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