دورية أكاديمية
Constructing a Machine Learning Model for Rapid Urban Flooding Forecast in Sloping Cities along the Yangtze River: A Case Study in Jiujiang
| العنوان: | Constructing a Machine Learning Model for Rapid Urban Flooding Forecast in Sloping Cities along the Yangtze River: A Case Study in Jiujiang |
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| المؤلفون: | Zhong Gao, Xiaoping Lu, Ruihong Chen, Minrui Guo, Xiaoxuan Wang |
| المصدر: | Water, Vol 16, Iss 12, p 1694 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Hydraulic engineering LCC:Water supply for domestic and industrial purposes |
| مصطلحات موضوعية: | flooding, hydrology, hydraulics, coupling, machine learning, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500 |
| الوصف: | Cities with sloping terrain are more susceptible to flooding during heavy rains. Traditional hydraulic models struggle to meet computational demands when addressing such emergencies. This study presented an integration of the one-dimensional Storm Water Management Model (SWMM) and the two-dimensional LISFLOOD-FP model, where the head difference at coupled manholes between the two models functioned as the connection. Based on its calculation results, this study extracted the characteristic parameters of the rainfall data, simplified the SVR calculation method and developed a high-efficiency solution for determining the maximum ponding depth. The cost time of this model was stable at approximately 1.0 min, 95% faster compared to the one from the mechanism model for 5 h simulation under the same working conditions. By conducting this case study in Jiujiang, China, the feasibility of this algorithm was well demonstrated. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 16121694 2073-4441 |
| Relation: | https://www.mdpi.com/2073-4441/16/12/1694; https://doaj.org/toc/2073-4441 |
| DOI: | 10.3390/w16121694 |
| URL الوصول: | https://doaj.org/article/0f29bfe6d5e6444485a574f13ab48296 |
| رقم الأكسشن: | edsdoj.0f29bfe6d5e6444485a574f13ab48296 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16121694 20734441 |
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| DOI: | 10.3390/w16121694 |