Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier
Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/169506 |
| _version_ | 1826112588627312640 |
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| author | Wang, Mo Fu, Xiaoping Zhang, Dongqing Chen, Furong Su, Jin Zhou, Shiqi Li, Jianjun Zhong, Yongming Tan, Soon Keat |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Wang, Mo Fu, Xiaoping Zhang, Dongqing Chen, Furong Su, Jin Zhou, Shiqi Li, Jianjun Zhong, Yongming Tan, Soon Keat |
| author_sort | Wang, Mo |
| collection | NTU |
| description | Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes. |
| first_indexed | 2024-10-01T03:09:31Z |
| format | Journal Article |
| id | ntu-10356/169506 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:09:31Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1695062023-07-21T15:33:36Z Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier Wang, Mo Fu, Xiaoping Zhang, Dongqing Chen, Furong Su, Jin Zhou, Shiqi Li, Jianjun Zhong, Yongming Tan, Soon Keat School of Civil and Environmental Engineering Engineering::Civil engineering Rural-Urban Fringe Urban Flooding Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes. Published version This research was funded by the Natural Science Foundation of Guangdong Province, China [grant number 2023A1515030158] and Science and Technology Program of Guangzhou, China [grant number 202201010431]. 2023-07-21T02:21:49Z 2023-07-21T02:21:49Z 2023 Journal Article Wang, M., Fu, X., Zhang, D., Chen, F., Su, J., Zhou, S., Li, J., Zhong, Y. & Tan, S. K. (2023). Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier. Sustainability, 15(7), 5740-. https://dx.doi.org/10.3390/su15075740 2071-1050 https://hdl.handle.net/10356/169506 10.3390/su15075740 2-s2.0-85152795653 7 15 5740 en Sustainability © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Civil engineering Rural-Urban Fringe Urban Flooding Wang, Mo Fu, Xiaoping Zhang, Dongqing Chen, Furong Su, Jin Zhou, Shiqi Li, Jianjun Zhong, Yongming Tan, Soon Keat Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title | Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title_full | Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title_fullStr | Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title_full_unstemmed | Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title_short | Urban flooding risk assessment in the rural-urban fringe based on a Bayesian classifier |
| title_sort | urban flooding risk assessment in the rural urban fringe based on a bayesian classifier |
| topic | Engineering::Civil engineering Rural-Urban Fringe Urban Flooding |
| url | https://hdl.handle.net/10356/169506 |
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