Existing landslide research is mostly limited to single deformation data, ignoring the influence of multidimensional environmental characteristics factors on landslide deformation, such as temperature, dew point temperature, precipitation, and spatiotemporal correlation. To address this issue, we propose a multivariate Transformer prediction model based on time series InSAR technology. The model integrates multidimensional environmental features and spatiotemporal correlation data, and uses wavelet transform to decompose deformation sequences into periodic and trend components. Different Transformer structures are constructed to predict each component. The results show that the prediction accuracy of this model is significantly better than that of traditional methods, with RMSE of 0.72 mm and R2 of 0.96. This research provides a new approach for landslide deformation prediction and is of great significance for improving early warning capabilities.
| 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 |