The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data
In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-condit...
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MDPI AG
2025-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/2/57 |
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| author | Julieber T. Bersabe Byong-Woon Jun |
| author_facet | Julieber T. Bersabe Byong-Woon Jun |
| author_sort | Julieber T. Bersabe |
| collection | DOAJ |
| description | In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. |
| format | Article |
| id | doaj-art-5be7c4ca67ce49bbab8509257ebf4db3 |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-5be7c4ca67ce49bbab8509257ebf4db32025-08-20T02:44:35ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-02-011425710.3390/ijgi14020057The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related DataJulieber T. Bersabe0Byong-Woon Jun1Department of Geography, Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Geography, Kyungpook National University, Daegu 41566, Republic of KoreaIn the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure.https://www.mdpi.com/2220-9964/14/2/57urban flood susceptibilitydrainage-related datamachine learningrandom forestsupport vector machineslogistic regression |
| spellingShingle | Julieber T. Bersabe Byong-Woon Jun The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data ISPRS International Journal of Geo-Information urban flood susceptibility drainage-related data machine learning random forest support vector machines logistic regression |
| title | The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data |
| title_full | The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data |
| title_fullStr | The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data |
| title_full_unstemmed | The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data |
| title_short | The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data |
| title_sort | machine learning based mapping of urban pluvial flood susceptibility in seoul integrating flood conditioning factors and drainage related data |
| topic | urban flood susceptibility drainage-related data machine learning random forest support vector machines logistic regression |
| url | https://www.mdpi.com/2220-9964/14/2/57 |
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