An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou
Cities along rivers are threatened by floods and waterlogging, and the relationship between rainstorms and floods is complex. The temporal and spatial distributions of rainstorms directly affect flood characteristics. The location of the rainstorm center determines the flood peaks, volumes, and proc...
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MDPI AG
2023-10-01
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Online Access: | https://www.mdpi.com/2073-4441/15/21/3755 |
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author | Yuanyuan Liu Yesen Liu Jiazhuo Wang Hancheng Ren Shu Liu Wencai Hu |
author_facet | Yuanyuan Liu Yesen Liu Jiazhuo Wang Hancheng Ren Shu Liu Wencai Hu |
author_sort | Yuanyuan Liu |
collection | DOAJ |
description | Cities along rivers are threatened by floods and waterlogging, and the relationship between rainstorms and floods is complex. The temporal and spatial distributions of rainstorms directly affect flood characteristics. The location of the rainstorm center determines the flood peaks, volumes, and processes. In this study, machine learning algorithms were introduced to analyze the rain–flood relationship in Luzhou City, Sichuan Province, China. The spatial and temporal patterns of rainstorms in the region were classified and extracted, and flood characteristics generated by various types of rainstorms were analyzed. In the first type, the center of the rainstorm was in the upper reaches of the Tuojiang River, and the resulting flood caused negligible damage to Luzhou. In the second type, the center of the rainstorm occurred in the Yangtze River Basin. Continuously high water levels in the Yangtze River, combined with local rainfall, supported urban drainage. In the third type, the rainstorm center occurred in the upper reaches of the Yangtze and Tuojiang rivers. During the flooding, rainfall from Yangtze River and Tuojiang River moved towards Luzhou together. The movement of the rainstorm center was consistent with the flood routing direction of the Yangtze and Tuojiang rivers, both of which continued to have high water levels. The flood risk is extremely high in this case, making it the riskiest rainfall process requiring prevention. |
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format | Article |
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institution | Directory Open Access Journal |
issn | 2073-4441 |
language | English |
last_indexed | 2024-03-11T11:19:23Z |
publishDate | 2023-10-01 |
publisher | MDPI AG |
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series | Water |
spelling | doaj.art-6051a7ecca12451bb49f3816d78546a42023-11-10T15:15:14ZengMDPI AGWater2073-44412023-10-011521375510.3390/w15213755An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of LuzhouYuanyuan Liu0Yesen Liu1Jiazhuo Wang2Hancheng Ren3Shu Liu4Wencai Hu5State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Academy of Urban Planning and Design, Beijing 100044, ChinaCollege of Water Sciences, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaThe Yi-Shu-Si River Basin Administration, H.R.C, Xuzhou 221018, ChinaCities along rivers are threatened by floods and waterlogging, and the relationship between rainstorms and floods is complex. The temporal and spatial distributions of rainstorms directly affect flood characteristics. The location of the rainstorm center determines the flood peaks, volumes, and processes. In this study, machine learning algorithms were introduced to analyze the rain–flood relationship in Luzhou City, Sichuan Province, China. The spatial and temporal patterns of rainstorms in the region were classified and extracted, and flood characteristics generated by various types of rainstorms were analyzed. In the first type, the center of the rainstorm was in the upper reaches of the Tuojiang River, and the resulting flood caused negligible damage to Luzhou. In the second type, the center of the rainstorm occurred in the Yangtze River Basin. Continuously high water levels in the Yangtze River, combined with local rainfall, supported urban drainage. In the third type, the rainstorm center occurred in the upper reaches of the Yangtze and Tuojiang rivers. During the flooding, rainfall from Yangtze River and Tuojiang River moved towards Luzhou together. The movement of the rainstorm center was consistent with the flood routing direction of the Yangtze and Tuojiang rivers, both of which continued to have high water levels. The flood risk is extremely high in this case, making it the riskiest rainfall process requiring prevention.https://www.mdpi.com/2073-4441/15/21/3755manifold learningmachine learningspatial–temporal rainstorm distributionfeature extractionrainstorm/flood relationshipLuzhou |
spellingShingle | Yuanyuan Liu Yesen Liu Jiazhuo Wang Hancheng Ren Shu Liu Wencai Hu An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou Water manifold learning machine learning spatial–temporal rainstorm distribution feature extraction rainstorm/flood relationship Luzhou |
title | An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou |
title_full | An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou |
title_fullStr | An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou |
title_full_unstemmed | An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou |
title_short | An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou |
title_sort | analysis of rainfall characteristics and rainfall flood relationships in cities along the yangtze river based on machine learning a case study of luzhou |
topic | manifold learning machine learning spatial–temporal rainstorm distribution feature extraction rainstorm/flood relationship Luzhou |
url | https://www.mdpi.com/2073-4441/15/21/3755 |
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