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Noisy multivariate prediction is a common challenge in fields such as environmental science, transportation, and industry. The core difficulty lies in balancing noise filtering with multi−scale feature capture. To address this, a hybrid model (Kalman−LSTM−Transformer) based on Kalman filter, long short−term memory (LSTM), and Transformer is proposed. This model captures local temporal and global dependencies while filtering noise, and integrates Bayesian optimization to achieve efficient and accurate prediction. Using open−pit mine dust concentration prediction as a case study, experiments based on 1−year of monitoring data demonstrate that the model outperforms baseline models, reducing the root mean square error (RMSE) by 21.70%–27.19% and the mean absolute error (MAE) by 6.68%–18.30%, while achieving a coefficient of determination (R2) of 0.934. Ablation experiments and hyperparameter analysis results further confirm the effectiveness of each module. The model exhibits transferability to similar scenarios, providing support for intelligent early warning and precision management across multiple domains.
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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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