Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators
The purpose of this research is to build a deep learning algorithm-based model that can use weather indicators to quantitatively predict financial losses associated with weather-related railroad accidents. Extreme weather events and weather disasters caused by global warming are happening with incre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/18/10418 |
| _version_ | 1797581354855038976 |
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| author | Kwang-Kyun Lim Ji-Myong Kim |
| author_facet | Kwang-Kyun Lim Ji-Myong Kim |
| author_sort | Kwang-Kyun Lim |
| collection | DOAJ |
| description | The purpose of this research is to build a deep learning algorithm-based model that can use weather indicators to quantitatively predict financial losses associated with weather-related railroad accidents. Extreme weather events and weather disasters caused by global warming are happening with increasing frequency worldwide, leading to substantial economic losses. Railways, which represent one of the most important means of transportation, are also affected by such weather events. However, empirical and quantitative studies examining losses stemming from weather conditions for railways have to this point been scarce. Hence, the present study collected and analyzed weather-induced railway accident data and meteorological factors (wind, precipitation, rainfall, etc.) from 2001 to 2021 with the aim of predicting financial losses caused by weather events; the ultimate goal is to help inform long-term strategies for effective recovery from railway accidents. Objective and scientific analysis was conducted in the present study by using a deep learning algorithm. The outcomes and framework of this research will offer crucial guidelines for efficient and sustainable railway maintenance. These results will also serve as a crucial point of reference for loss quantification studies and other facility management studies. |
| first_indexed | 2024-03-10T23:04:13Z |
| format | Article |
| id | doaj.art-246e4c7ae2b74dbd80858d1cc0f876b5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:13Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-246e4c7ae2b74dbd80858d1cc0f876b52023-11-19T09:27:27ZengMDPI AGApplied Sciences2076-34172023-09-0113181041810.3390/app131810418Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather IndicatorsKwang-Kyun Lim0Ji-Myong Kim1Department of Railroad Management, Songwon University, Gwangju 61756, Republic of KoreaDepartment of Architectural Engineering, Mokpo National University, Muan 58554, Republic of KoreaThe purpose of this research is to build a deep learning algorithm-based model that can use weather indicators to quantitatively predict financial losses associated with weather-related railroad accidents. Extreme weather events and weather disasters caused by global warming are happening with increasing frequency worldwide, leading to substantial economic losses. Railways, which represent one of the most important means of transportation, are also affected by such weather events. However, empirical and quantitative studies examining losses stemming from weather conditions for railways have to this point been scarce. Hence, the present study collected and analyzed weather-induced railway accident data and meteorological factors (wind, precipitation, rainfall, etc.) from 2001 to 2021 with the aim of predicting financial losses caused by weather events; the ultimate goal is to help inform long-term strategies for effective recovery from railway accidents. Objective and scientific analysis was conducted in the present study by using a deep learning algorithm. The outcomes and framework of this research will offer crucial guidelines for efficient and sustainable railway maintenance. These results will also serve as a crucial point of reference for loss quantification studies and other facility management studies.https://www.mdpi.com/2076-3417/13/18/10418weather-induced railroad accidentweather indicatorsfinancial lossesdeep learning algorithmloss prediction |
| spellingShingle | Kwang-Kyun Lim Ji-Myong Kim Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators Applied Sciences weather-induced railroad accident weather indicators financial losses deep learning algorithm loss prediction |
| title | Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators |
| title_full | Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators |
| title_fullStr | Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators |
| title_full_unstemmed | Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators |
| title_short | Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators |
| title_sort | financial loss assessment for weather induced railway accidents based on a deep learning technique using weather indicators |
| topic | weather-induced railroad accident weather indicators financial losses deep learning algorithm loss prediction |
| url | https://www.mdpi.com/2076-3417/13/18/10418 |
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