Weather Forecasting and Analysis with LSTM Based on Deep learning

DOI

10.2991/978-94-6463-540-9_19How to use a DOI?

Keywords

LSTM; RNN; Sequence Prediction

Abstract

Weather forecasting is paramount for various sectors, fueling growing interest in leveraging machine learning for predictive weather analysis. The model proposed in this paper represents a significant advancement in this domain by integrating Long Short-Term Memory (LSTM) to augment the capabilities of Recurrent Neural Networks (RNN). By capitalizing on LSTM’s unique architectural features, the model excels in processing extended data sequences, thus bolstering learning and prediction accuracy. Empirical results underscore the model’s superiority over traditional forecasting methods, characterized by substantial error margins. This study not only underscores LSTM’s prowess in sequence prediction but also sheds light on its practical utility, offering valuable insights into LSTM’s functioning. Moreover, the findings of this research serve to deepen understanding of sequence prediction methodologies, paving the way for addressing more complex predictive challenges in the future. Ultimately, the integration of LSTM into the RNN framework represents an important step towards improving the reliability of weather forecasting models and refined predictions of forecasts.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fengyuan Zhang
PY  - 2024
DA  - 2024/10/16
TI  - Weather Forecasting and Analysis with LSTM Based on Deep learning
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 165
EP  - 172
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_19
DO  - 10.2991/978-94-6463-540-9_19
ID  - Zhang2024
ER  -