Research Analysis of Optimization of Temperature Prediction Model Based on Random Forest

DOI

10.2991/978-94-6239-648-7_51How to use a DOI?

Keywords

Temperature Prediction; Random Forest; Hyperparameter Tuning; Machine Learning

Abstract

Temperature prediction plays a crucial role in meteorological analysis, scientific planning of agricultural production, precise allocation of energy management and so on. But traditional temperature prediction methods often fall short in terms of prediction accuracy and fail to meet expectations. To address this challenging issue, this study innovatively proposes a new temperature prediction approach based on in-depth improvement of the random forest model. High-quality feature sets rich in information are constructed through data preprocessing procedures and feature engineering. The optimal parameter configuration is explored using a hyperparameter tuning strategy, and the improved random forest regression model is employed to achieve accurate temperature prediction. Outlier removal, in-depth analysis of feature importance, and rigorous considerations of cross-validation are integrated to enhance the robustness of the model. On the independent test set, this method demonstrates remarkable experimental results: the prediction accuracy reaches a high level of 94.22%, and the Root Mean Squared Error (RMSE) drops sharply to 4.85, showing a significant performance improvement compared with the baseline model. Undoubtedly, this study confirms that the optimized random forest model is highly effective and robust in the field of temperature prediction, thereby providing a reliable machine learning solution for in-depth analysis of meteorological data.

Copyright

© 2026 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.

Download article (PDF)

TY  - CONF
AU  - Tianhua Jiang
PY  - 2026
DA  - 2026/04/24
TI  - Research Analysis of Optimization of Temperature Prediction Model Based on Random Forest
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 463
EP  - 469
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_51
DO  - 10.2991/978-94-6239-648-7_51
ID  - Jiang2026
ER  -