Abstract: With the advancement of environmental monitoring technologies， accurately identifying anomalies in data has become a crucial challenge. This study introduces a method combining Long Short-Term Memory (LSTM) networks and Random Forest models for predicting and reconstructing environmental feature data， thereby enabling effective anomaly monitoring. Initially， the LSTM model is used to forecast time series data for environmental features such as wind speed and direction. Subsequently， these forecasts are used as inputs to apply a Random Forest model for predicting axial force. The research indicates that by reconstructing features， the accuracy of axial force predictions can be significantly enhanced compared to direct anomaly detection methods. The R2 values， Mean Absolute Error (MAE)， and Root Mean Squared Error (RMSE) for data with reconstructed features surpass those for data with original features. Particularly， the improvement from an R2 value of 0.921 to 0.956 underscores a significant enhancement in the model's data fitting capability.

Key words: Long Short-Term Memory networks, Random Forest, anomaly monitoring, feature reconstruction