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Water Temperature Prediction Study Using Feature Extraction and Reconstruction based on LSTM-Autoencoder

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(11), pp.13-20
  • DOI : 10.9708/jksci.2023.28.11.013
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 6, 2023
  • Accepted : November 2, 2023
  • Published : November 30, 2023

SONG GUDEUK 1 Su-Hyun Park 2

1부산대학교
2동서대학교

Accredited

ABSTRACT

In this paper, we propose a water temperature prediction method using feature extraction and reconstructed data based on LSTM-Autoencoder. We used multivariate time series data such as sea surface water temperature in the Naksan area of the East Sea where the cold water zone phenomenon occurred, and wind direction and wind speed that affect water temperature. Using the LSTM-Autoencoder model, we used three types of data: feature data extracted through dimensionality reduction of the original data combined with multivariate data of the original data, reconstructed data, and original data. The three types of data were trained by the LSTM model to predict sea surface water temperature and evaluated the accuracy. As a result, the sea surface water temperature prediction accuracy using feature extraction of LSTM-Autoencoder confirmed the best performance with MAE 0.3652, RMSE 0.5604, MAPE 3.309%. The result of this study are expected to be able to prevent damage from natural disasters by improving the prediction accuracy of sea surface temperature changes rapidly such as the cold water zone.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.