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Outline
A multi-neural-network weighted ensemble model for landslide displacement prediction is constructed by integrating three machine learning models: support vector regression (SVR), back propagation neural network (BPNN), and linear regression neural network (LRNN). The model development process primarily consists of two components: individual model prediction and multi-neural-network weighting. Instead of emphasizing prediction errors from all historical moments preceding the current prediction point, this ensemble model focuses on the proximity period around the current prediction point. Optimal weight coefficients for individual prediction models are calculated using the least squares method based on observed data, followed by weighted summation of predictions from individual models at the current point using these weights, ultimately achieving high-precision forecasting results. Comparative tests conducted on Beidou/GNSS monitoring data from the Dangchuan landslide mass at Heifangtai in Gansu province—a typical loess landslide area in China—demonstrate that this proposed ensemble model exhibits superior prediction accuracy compared to single-model approaches, simple averaging ensemble models, and multi-neural-network switching models. The proposed methodology shows significant potential for practical applications in landslide displacement prediction.
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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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