Building loss assessment using deep learning algorithm from typhoon Rusa
Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023105329 |
| _version_ | 1797337059486072832 |
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| author | Ji-Myong Kim Junseo Bae Manik Das Adhikari Sang-Guk Yum |
| author_facet | Ji-Myong Kim Junseo Bae Manik Das Adhikari Sang-Guk Yum |
| author_sort | Ji-Myong Kim |
| collection | DOAJ |
| description | Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the increasing damage, but the results have yet to meet market demand. Therefore, this study proposes a method that uses a deep learning algorithm to predict the damage caused by typhoons. Model development is based on a Deep Neural Network (DNN) algorithm, and learning data is obtained by fine-tuning the network structure and hyperparameters; the amount of damage caused by Typhoon Rusa was known as training data. The constructed DNN model underwent evaluation and validation by computation of mean absolute error (MAE) and root mean square error (RMSE). Furthermore, a comparative analysis was conducted to confirm the applicability of the proposed framework against a traditional multi-regression model to ensure the model's accuracy and resilience. Finally, this study offers a novel approach to predicting typhoon damage using advanced deep-learning techniques. Subsequently, government disaster management officials, facility managers, and insurance companies can utilize this method to accurately predict the extent of damage caused by typhoons. Preventive actions such as improved risk assessment, expanded insurance companies, and enhanced disaster responses plans can be implemented using these outcomes. Ultimately, the proposed model will help to reduce typhoon damage and strengthen general resilience to climate crises. |
| first_indexed | 2024-03-08T09:03:55Z |
| format | Article |
| id | doaj.art-d9996af65daa43aba250cf405d659644 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T09:03:55Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-d9996af65daa43aba250cf405d6596442024-02-01T06:31:27ZengElsevierHeliyon2405-84402024-01-01101e23324Building loss assessment using deep learning algorithm from typhoon RusaJi-Myong Kim0Junseo Bae1Manik Das Adhikari2Sang-Guk Yum3Department of Architectural Engineering, Mokpo National University, Mokpo, 58554, South KoreaDivision of Smart Cities, Korea University, Sejong, 30019, South KoreaDepartment of Civil Engineering, Gangneung-Wonju National University, Gangneung, 25457, South KoreaDepartment of Civil Engineering, Gangneung-Wonju National University, Gangneung, 25457, South Korea; Corresponding author.Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the increasing damage, but the results have yet to meet market demand. Therefore, this study proposes a method that uses a deep learning algorithm to predict the damage caused by typhoons. Model development is based on a Deep Neural Network (DNN) algorithm, and learning data is obtained by fine-tuning the network structure and hyperparameters; the amount of damage caused by Typhoon Rusa was known as training data. The constructed DNN model underwent evaluation and validation by computation of mean absolute error (MAE) and root mean square error (RMSE). Furthermore, a comparative analysis was conducted to confirm the applicability of the proposed framework against a traditional multi-regression model to ensure the model's accuracy and resilience. Finally, this study offers a novel approach to predicting typhoon damage using advanced deep-learning techniques. Subsequently, government disaster management officials, facility managers, and insurance companies can utilize this method to accurately predict the extent of damage caused by typhoons. Preventive actions such as improved risk assessment, expanded insurance companies, and enhanced disaster responses plans can be implemented using these outcomes. Ultimately, the proposed model will help to reduce typhoon damage and strengthen general resilience to climate crises.http://www.sciencedirect.com/science/article/pii/S2405844023105329Climate changeExtreme weather eventsTyphoon RusaWet typhoonDeep learning algorithm |
| spellingShingle | Ji-Myong Kim Junseo Bae Manik Das Adhikari Sang-Guk Yum Building loss assessment using deep learning algorithm from typhoon Rusa Heliyon Climate change Extreme weather events Typhoon Rusa Wet typhoon Deep learning algorithm |
| title | Building loss assessment using deep learning algorithm from typhoon Rusa |
| title_full | Building loss assessment using deep learning algorithm from typhoon Rusa |
| title_fullStr | Building loss assessment using deep learning algorithm from typhoon Rusa |
| title_full_unstemmed | Building loss assessment using deep learning algorithm from typhoon Rusa |
| title_short | Building loss assessment using deep learning algorithm from typhoon Rusa |
| title_sort | building loss assessment using deep learning algorithm from typhoon rusa |
| topic | Climate change Extreme weather events Typhoon Rusa Wet typhoon Deep learning algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023105329 |
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