The Research of Key Roles of Logistic Regression Model and Random Forest Model in Numerical Weather Prediction

Yishun Zhang

2025

Abstract

Weather forecasting has evolved from ancient observations of clouds and wind patterns to a sophisticated science driven by advanced technology. Today, it plays a crucial role in disaster mitigation, agriculture, transportation, and daily life. Modern forecasting relies on satellite imagery, radar systems, supercomputers, and complex algorithms to predict weather phenomena with increasing accuracy. This paper focuses on the application of Numerical Weather Prediction (NWP) models and machine learning techniques like logistic regression in analyzing weather patterns across diverse U.S. regions, particularly in complex terrains. NWP models process vast atmospheric data to simulate weather systems, while logistic regression helps classify and predict extreme weather events. Their combined use enhances forecast precision in challenging areas such as mountainous zones and coastlines, where traditional methods often struggle. These technological advancements not only improve early warning systems but also contribute to more resilient infrastructure planning and better emergency preparedness, ultimately saving lives and reducing economic losses.

Paper Citation

in Harvard Style

Zhang Y. (2025). The Research of Key Roles of Logistic Regression Model and Random Forest Model in Numerical Weather Prediction. In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-774-0, SciTePress, pages 644-648. DOI: 10.5220/0013848500004708

in Bibtex Style

@conference{iampa25,
author={Yishun Zhang},
title={The Research of Key Roles of Logistic Regression Model and Random Forest Model in Numerical Weather Prediction},
booktitle={Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA},
year={2025},
pages={644-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013848500004708},
isbn={978-989-758-774-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA
TI - The Research of Key Roles of Logistic Regression Model and Random Forest Model in Numerical Weather Prediction
SN - 978-989-758-774-0
AU - Zhang Y.
PY - 2025
SP - 644
EP - 648
DO - 10.5220/0013848500004708
PB - SciTePress