分形维数—CONVLSTM模型在合肥市流感发病人数预测中的应用
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张胜,硕士研究生,研究方向:公共卫生统计

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R181

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Application of fractal dimension CONVLSTM model in predicting the number of influenza cases in Hefei City
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    摘要:

    目的 根据合肥市流行性感冒(流感)与气象因素以及PM2.5变量之间的数据进行多变量建模预测,以期为多变量建模预测研究方法提供新思路。方法 将PM2.5数据转化为分形维数数据后与气象数据作为特征引入到ConvLSTM模型中,与传统的ARIMAX模型以及多变量LSTM模型进行比较分析。结果 ARIMAX模型测试集MAE=95.75,RMSE=176.72,IA=0.396642;多变量LSTM测试集MAE=22.18,RMSE=43.06,IA=0.974611;分形参数-CONVLSTM模型测试集MAE=17.37,RMSE=32.25,IA=0.988149。结论 分形维数捕捉了PM2.5浓度的复杂性和自相似性,为模型提供了更丰富的特征信息,分形维数-ConvLSTM模型在预测准确性上优于传统的ARIMAX模型和多变量LSTM模型,可以用于流感发病人数的预测。

    Abstract:

    Objective To develop a multivariate modeling and prediction approach using the data of influenza incidence, meteorological factors and PM2.5 variables in Hefei City, and to provide new insights into multivariate modeling and prediction methods. Methods PM2.5 data were transformed into fractal dimension data and, along with meteorological data, were incorporated into a ConvLSTM model. The performance of this model was compared with traditional ARIMAX and multivariate LSTM models. Results The ARIMAX model’s testing set Mean Absolute Error (MAE) was 95.75, Root Mean Square Error (RMSE) was 176.72, and Index of Agreement (IA) was 0.396642. The multivariate LSTM model's testing set MAE was 22.18, RMSE was 43.06, and IA was 0.974611. For the fractal dimension-based ConvLSTM model, the testing set MAE was 17.37, RMSE was 32.25, and IA was 0.988149. Conclusion The fractal dimension effectively captures the complexity and self-similarity of PM2.5 concentration, providing the model with richer feature information. The fractal dimension-based ConvLSTM model significantly outperforms the traditional ARIMAX model and the multivariate LSTM model in prediction accuracy and can be used for predicting the number of influenza cases.

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  • 收稿日期:2024-12-03
  • 最后修改日期:2024-12-03
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  • 在线发布日期: 2025-01-15
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