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This paper uses historical landslide and hydrogeological data from Anhui province as the original dataset, employing five typical machine learning models and selects nine environmental disaster-inducing factors as initial input variables. To explore the impact of different machine learning input variable selection combinations on the performance of various models, multiple factor selection methods and several evaluation metrics are used to examine the prediction accuracy of each algorithm model under different selection combinations for landslide susceptibility. The evaluation results show that the combination of the full selection set and the lightweight gradient boosting machine (LightGBM). model yields the best performance. Therefore, this combination is chosen for the landslide susceptibility mapping in the study area. Moreover, the study suggests that factor selection for machine learning inputs does not necessarily improve model performance in landslide susceptibility studies of the research area. Additionally, ensemble models outperform individual estimator models, and LightGBM, using a leaf-node-based decision tree algorithm, shows improved performance compared to other ensemble algorithms.
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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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