An Overview of Application of Machine Learning Models in Urban Flood Simulation and Forecasting
In recent years, the frequent occurrence of extreme rainstorms has been prone to causing urban flood disasters, directly affecting people's lives and property safety. To enhance the level of urban emergency management and effectively formulate urban disaster pre vention and mitigation strategie...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2025-01-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.01.002 |
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| Summary: | In recent years, the frequent occurrence of extreme rainstorms has been prone to causing urban flood disasters, directly affecting people's lives and property safety. To enhance the level of urban emergency management and effectively formulate urban disaster pre vention and mitigation strategies, there is an urgent need to develop an accurate and efficient urban flood simulation and forecasting model. The rapidly developed artificial intelligence technology gradually shows great potential and value in urban flood simulation and forecasting. By systematically searching and synthesizing the relevant literature published in the recent decade, this paper sorted out the background, trend causes, and research hotspots of urban floods. Additionally, this paper also focused on the research on urban flood simulation and forecasting models based on machine learning algorithms published in the recent five years, summarized the technical processes for urban flood simulation and forecasting models based on machine learning algorithms, and proposed several current technological bottlenecks and potential solutions. Finally, this study summarized the technical advantages, further development directions, and application prospects of machine learning models, so as to provide a reference for future research on urban flood simulation and forecasting and disaster prevention and mitigation. |
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| ISSN: | 1001-9235 |