Catastrophe risk assessment based on arima and decision tree model

Yuan Sun; Tingting Yan; Enlin Xu; Wenliang Dai; Yuhang He

doi:10.1117/12.3035101

21 July 2024 Catastrophe risk assessment based on arima and decision tree model

Yuan Sun, Tingting Yan, Enlin Xu, Wenliang Dai, Yuhang He

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132193W (2024) https://doi.org/10.1117/12.3035101
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

In this study, a catastrophe risk assessment system is established, which includes a catastrophe risk assessment model and an ARIMA risk prediction model. Taking Fukuoka as the study area, the risk factors are calculated for the region from 1950 to 2022. On this basis, a risk factor prediction model is constructed to dynamically transform the risk factors. The risk factors of the area from 2023 to 2029 are predicted, and the preliminary analysis of the catastrophe risk assessment is realized. Secondly, since the ARIMA risk factor prediction model only analyzes risk factors and time series, it is difficult for the model to feedback the catastrophe risk factors implied by the natural geographical environment and economic conditions of a certain place at a certain time. Therefore, this study constructs a multi-factor risk factor decision tree regression model based on physical geographic data and socio-economic data, further improves the policy risk assessment system. Finally, we predict the risk factors in Texas and provide a decision-making basis for catastrophe risk assessment.

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

Citation Download Citation

Yuan Sun, Tingting Yan, Enlin Xu, Wenliang Dai, and Yuhang He "Catastrophe risk assessment based on arima and decision tree model", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132193W (21 July 2024); https://doi.org/10.1117/12.3035101

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