A predictive model for weather-related risks utilizing factor analysis and long short-term memory networks

Shuhan Ye

doi:10.1117/12.3035459

21 July 2024 A predictive model for weather-related risks utilizing factor analysis and long short-term memory networks

Shuhan Ye

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132193Z (2024) https://doi.org/10.1117/12.3035459
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

This study introduces a holistic approach to outcome prediction using multiple indicators. We develop an innovative methodology that combines factor analysis for weight determination of variables, facilitating dimension reduction, with Long Short-Term Memory (LSTM) neural networks for comprehensive evaluation and precise forecasts. To assess the efficacy of our proposed technique, we undertook empirical research in the domain of weather-related risk—a field of growing importance that demands further investigation. The findings indicate that our predictive model, which merges factor analysis with LSTM, excels in multivariate forecasting, yielding relatively consistent results. Moreover, it underscores the projection that the impact of weather-related disasters is likely to escalate in tandem with population and economic growth.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuhan Ye "A predictive model for weather-related risks utilizing factor analysis and long short-term memory networks", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132193Z (21 July 2024); https://doi.org/10.1117/12.3035459

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available