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Comparative Analysis of RNN Architectures and Activation Functions with Attention Mechanisms for Mars Weather Prediction

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.1-9
  • DOI : 10.9708/jksci.2024.29.10.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 27, 2024
  • Accepted : October 18, 2024
  • Published : October 31, 2024

Jaehyeok Jo 1 Yunho Sin 2 Bo-Young Kim 2 Jihoon Moon 1

1순천향대학교
2아산중학교

Accredited

ABSTRACT

In this paper, we propose a comparative analysis to evaluate the impact of activation functions and attention mechanisms on the performance of time-series models for Mars meteorological data. Mars meteorological data are nonlinear and irregular due to low atmospheric density, rapid temperature variations, and complex terrain. We use long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) architectures to evaluate the effectiveness of different activation functions and attention mechanisms. The activation functions tested include rectified linear unit (ReLU), leaky ReLU, exponential linear unit (ELU), Gaussian error linear unit (GELU), Swish, and scaled ELU (SELU), and model performance was measured using mean absolute error (MAE) and root mean square error (RMSE) metrics. Our results show that the integration of attentional mechanisms improves both MAE and RMSE, with Swish and ReLU achieving the best performance for minimum temperature prediction. Conversely, GELU and ELU were less effective for pressure prediction. These results highlight the critical role of selecting appropriate activation functions and attention mechanisms in improving model accuracy for complex time-series forecasting.

Citation status

* References for papers published after 2023 are currently being built.

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