Anomaly Detection of Environmental Features Based on LSTM-Reconstructed Data

  • ZHOU Lei1 ,
  • SU Xin2 ,
  • ZHANG Qi2 ,
  • HUANG Yi2
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  • 1. Offshore Oil Engineering Co., Ltd., Tianjin 300451, China; 2. School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116086, Liaoning China

Online published: 2025-02-21

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.

Cite this article

ZHOU Lei1 , SU Xin2 , ZHANG Qi2 , HUANG Yi2 . Anomaly Detection of Environmental Features Based on LSTM-Reconstructed Data[J]. Ocean Engineering Equipment and Technology, 2024 , 11(4) : 1 -7 . DOI: 10.12087/oeet.2095-7297.2024.04.01

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