Enhancing Weather Forecasting with Machine Learning and Deep Learning: A Comparative Study of Predictive Models

DOI

10.2991/978-94-6463-716-8_33How to use a DOI?

Keywords

Weather Forecasting; Machine Learning; Deep Learning; Predictive Models; Atmospheric Data

Abstract

Weather prediction plays a central role in the welfare of the society and daily economic activities. Accurate conventional NWP methods are currently widely employed but are associated with constraint in managing the non-linearity of the ambiance. This paper focuses on realizing the potential of supervised Machine Learning (ML), and Deep Learning (DL) to improve weather forecasting. A comparison of several selected models of ML and DL is made based on several factors, including accuracy of prediction, model stability, and computational complexity. The approach is validated in the context of a real scenario, showcasing on how deep learning models can outperform traditional methods achieving higher accuracy and better coping with atmospheric conditions, especially when hybrid models are employed.

Copyright

© 2025 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  - Gracy Markan
AU  - Shivani Kamboj
PY  - 2025
DA  - 2025/05/26
TI  - Enhancing Weather Forecasting with Machine Learning and Deep Learning: A Comparative Study of Predictive Models
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 411
EP  - 422
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_33
DO  - 10.2991/978-94-6463-716-8_33
ID  - Markan2025
ER  -