A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula

Frontiers in Earth Science

Earth Science

Ji-Myong Kim¹  Manik Das Adhikari²  Sang-Guk Yum²  Junseo Bae³ 

[1] Department of Architectural Engineering, Mokpo National University, Mokpo, Republic of Korea;Department of Civil Engineering, Gangneung-Wonju National University, Gangneung, Republic of Korea;Division of Smart Cities, Korea University, Sejong, Republic of Korea;

关键词: deep learning algorithm;    catastrophe model;    building loss;    typhoon;    the Korean Peninsula;   

DOI  :  10.3389/feart.2023.1136346

 received in 2023-01-03, accepted in 2023-07-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers.

【 授权许可】

Unknown   
Copyright © 2023 Kim, Bae, Adhikari and Yum.