Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China
Urban waterlogging is a major natural disaster in the process of urbanization. It is of great significance to carry out the analysis of influencing factors and susceptibility assessment of urban waterlogging for related prevention and control. However, the relationship between urban waterlogging and...
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1042088/full |
| _version_ | 1797831071799181312 |
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| author | Juchao Zhao Juchao Zhao Juchao Zhao Jin Wang Jin Wang Jin Wang Zaheer Abbas Zaheer Abbas Zaheer Abbas Yao Yang Yao Yang Yao Yang Yaolong Zhao Yaolong Zhao Yaolong Zhao |
| author_facet | Juchao Zhao Juchao Zhao Juchao Zhao Jin Wang Jin Wang Jin Wang Zaheer Abbas Zaheer Abbas Zaheer Abbas Yao Yang Yao Yang Yao Yang Yaolong Zhao Yaolong Zhao Yaolong Zhao |
| author_sort | Juchao Zhao |
| collection | DOAJ |
| description | Urban waterlogging is a major natural disaster in the process of urbanization. It is of great significance to carry out the analysis of influencing factors and susceptibility assessment of urban waterlogging for related prevention and control. However, the relationship between urban waterlogging and different influencing factors is often complicated and nonlinear. Traditional regression analysis methods have shortcomings in dealing with high-dimensional nonlinear issues. Gradient Boosting Decision Tree (GBDT) is an excellent ensemble learning algorithm that is highly flexible and efficient, capable of handling complex non-linear relationships, and has achieved significant results in many fields. This paper proposed a technical framework for quantitative analysis and susceptibility assessment on influencing factors of urban waterlogging based on the GBDT in a case study in Guangzhou city, China. Main factors and indicators affecting urban waterlogging in terrain and topography, impervious surface, vegetation coverage, drainage facilities, rivers, etc., were selected for the GBDT. The results demonstrate that: (1) GBDT performs well, with an overall accuracy of 83.5% and a Kappa coefficient of 0.669. (2) Drainage density, impervious surface, and NDVI are the most important influencing factors resulting in rainstorm waterlogging, with a total contribution of 85.34%. (3) The overall distribution of urban waterlogging susceptibility shows a characteristic of “high in the southwest and low in the northeast”, in which the high-susceptibility areas are mainly distributed in Yuexiu District (34%), followed by Liwan District (22%) and Haizhu District (20%). To mitigate the impact of frequent urban flooding disasters, future measures should focus on strengthening drainage networks, such as optimizing impervious surface spatial patterns, controlling construction activities in high-risk areas, and preventing excessive development of green spaces. |
| first_indexed | 2024-04-09T13:46:11Z |
| format | Article |
| id | doaj.art-9de7f8f6a0304880b08baa031962fe15 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-09T13:46:11Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-9de7f8f6a0304880b08baa031962fe152023-05-09T04:24:15ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-05-011110.3389/feart.2023.10420881042088Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, ChinaJuchao Zhao0Juchao Zhao1Juchao Zhao2Jin Wang3Jin Wang4Jin Wang5Zaheer Abbas6Zaheer Abbas7Zaheer Abbas8Yao Yang9Yao Yang10Yao Yang11Yaolong Zhao12Yaolong Zhao13Yaolong Zhao14School of Geography, South China Normal University, Guangzhou, ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaGuangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou, ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaGuangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou, ChinaFaculty of Engineering, Beidou Research Institute, South China Normal University, Foshan, ChinaSchool of Geography, South China Normal University, Guangzhou, ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaGuangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou, ChinaSchool of Geography, South China Normal University, Guangzhou, ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaGuangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou, ChinaSchool of Geography, South China Normal University, Guangzhou, ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaGuangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou, ChinaUrban waterlogging is a major natural disaster in the process of urbanization. It is of great significance to carry out the analysis of influencing factors and susceptibility assessment of urban waterlogging for related prevention and control. However, the relationship between urban waterlogging and different influencing factors is often complicated and nonlinear. Traditional regression analysis methods have shortcomings in dealing with high-dimensional nonlinear issues. Gradient Boosting Decision Tree (GBDT) is an excellent ensemble learning algorithm that is highly flexible and efficient, capable of handling complex non-linear relationships, and has achieved significant results in many fields. This paper proposed a technical framework for quantitative analysis and susceptibility assessment on influencing factors of urban waterlogging based on the GBDT in a case study in Guangzhou city, China. Main factors and indicators affecting urban waterlogging in terrain and topography, impervious surface, vegetation coverage, drainage facilities, rivers, etc., were selected for the GBDT. The results demonstrate that: (1) GBDT performs well, with an overall accuracy of 83.5% and a Kappa coefficient of 0.669. (2) Drainage density, impervious surface, and NDVI are the most important influencing factors resulting in rainstorm waterlogging, with a total contribution of 85.34%. (3) The overall distribution of urban waterlogging susceptibility shows a characteristic of “high in the southwest and low in the northeast”, in which the high-susceptibility areas are mainly distributed in Yuexiu District (34%), followed by Liwan District (22%) and Haizhu District (20%). To mitigate the impact of frequent urban flooding disasters, future measures should focus on strengthening drainage networks, such as optimizing impervious surface spatial patterns, controlling construction activities in high-risk areas, and preventing excessive development of green spaces.https://www.frontiersin.org/articles/10.3389/feart.2023.1042088/fullurban waterlogginginfluencing factorsGuangzhoususceptibility assessmentensemble learning |
| spellingShingle | Juchao Zhao Juchao Zhao Juchao Zhao Jin Wang Jin Wang Jin Wang Zaheer Abbas Zaheer Abbas Zaheer Abbas Yao Yang Yao Yang Yao Yang Yaolong Zhao Yaolong Zhao Yaolong Zhao Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China Frontiers in Earth Science urban waterlogging influencing factors Guangzhou susceptibility assessment ensemble learning |
| title | Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China |
| title_full | Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China |
| title_fullStr | Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China |
| title_full_unstemmed | Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China |
| title_short | Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China |
| title_sort | ensemble learning analysis of influencing factors on the distribution of urban flood risk points a case study of guangzhou china |
| topic | urban waterlogging influencing factors Guangzhou susceptibility assessment ensemble learning |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1042088/full |
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