Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information
Abstract Worldwide, catastrophic landslides are occurring as a result of abnormal climatic conditions. Since a landslide is caused by a combination of the triggers of rainfall and the vulnerability of spatial information, a study that can suggest a method to analyze the complex relationship between...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21795-z |
| _version_ | 1811233622291644416 |
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| author | Jae-Joon Lee Moon-Soo Song Hong-Sik Yun Sang-Guk Yum |
| author_facet | Jae-Joon Lee Moon-Soo Song Hong-Sik Yun Sang-Guk Yum |
| author_sort | Jae-Joon Lee |
| collection | DOAJ |
| description | Abstract Worldwide, catastrophic landslides are occurring as a result of abnormal climatic conditions. Since a landslide is caused by a combination of the triggers of rainfall and the vulnerability of spatial information, a study that can suggest a method to analyze the complex relationship between the two factors is required. In this study, the relationship between complex factors (rainfall period, accumulated rainfall, and spatial information characteristics) was designed as a system dynamics model as variables to check the possibility of occurrence of vulnerable areas according to the rainfall characteristics that change in real-time. In contrast to the current way of predicting the collapse time by analysing rainfall data, the developed model can set the precipitation period during rainfall. By setting the induced rainfall period, the researcher can then assess the susceptibility of the landslide-vulnerable area. Further, because the geospatial information features and rainfall data for the 672 h before the landslide's occurrence were combined, the results of the susceptibility analysis could be determined for each topographical characteristic according to the rainfall period and cumulative rainfall change. Third, by adjusting the General cumulative rainfall period (DG) and Inter-event time definition (IETD), the preceding rainfall period can be adjusted, and desired results can be obtained. An analysis method that can solve complex relationships can contribute to the prediction of landslide warning times and expected occurrence locations. |
| first_indexed | 2024-04-12T11:23:54Z |
| format | Article |
| id | doaj.art-01afb69f17574b5b868673e7da90c29f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T11:23:54Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-01afb69f17574b5b868673e7da90c29f2022-12-22T03:35:17ZengNature PortfolioScientific Reports2045-23222022-11-0112112010.1038/s41598-022-21795-zDynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial informationJae-Joon Lee0Moon-Soo Song1Hong-Sik Yun2Sang-Guk Yum3Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan UniversityInterdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan UniversityInterdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan UniversityDepartment of Civil Engineering, Gangneung-Wonju National UniversityAbstract Worldwide, catastrophic landslides are occurring as a result of abnormal climatic conditions. Since a landslide is caused by a combination of the triggers of rainfall and the vulnerability of spatial information, a study that can suggest a method to analyze the complex relationship between the two factors is required. In this study, the relationship between complex factors (rainfall period, accumulated rainfall, and spatial information characteristics) was designed as a system dynamics model as variables to check the possibility of occurrence of vulnerable areas according to the rainfall characteristics that change in real-time. In contrast to the current way of predicting the collapse time by analysing rainfall data, the developed model can set the precipitation period during rainfall. By setting the induced rainfall period, the researcher can then assess the susceptibility of the landslide-vulnerable area. Further, because the geospatial information features and rainfall data for the 672 h before the landslide's occurrence were combined, the results of the susceptibility analysis could be determined for each topographical characteristic according to the rainfall period and cumulative rainfall change. Third, by adjusting the General cumulative rainfall period (DG) and Inter-event time definition (IETD), the preceding rainfall period can be adjusted, and desired results can be obtained. An analysis method that can solve complex relationships can contribute to the prediction of landslide warning times and expected occurrence locations.https://doi.org/10.1038/s41598-022-21795-z |
| spellingShingle | Jae-Joon Lee Moon-Soo Song Hong-Sik Yun Sang-Guk Yum Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information Scientific Reports |
| title | Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information |
| title_full | Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information |
| title_fullStr | Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information |
| title_full_unstemmed | Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information |
| title_short | Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information |
| title_sort | dynamic landslide susceptibility analysis that combines rainfall period accumulated rainfall and geospatial information |
| url | https://doi.org/10.1038/s41598-022-21795-z |
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