Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data

Guozheng Zhi; Zhenliang Liao; Wenchong Tian; Jiang Wu

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Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data

机译：与3D可视化方法耦合PP-PSO算法和建筑数据的城市洪水风险评估与分析

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摘要

Due to the influence of buildings on the distribution of flood and their economic and social attributes, 3D spatial information such as the size of buildings and the flooded ratio of buildings relative to their height has an increasing impact on urban flood risk. However, existing flood risk assessment methods mainly use data in 2D and analysis methods are mostly 2D. In this study, flood variation processes were analyzed in the form of 3D dynamic visualization by coupling an urban drainage model and a flood simulation model with 3D visualization methods. By further combining with 3D building models, the 3D spatial information of buildings related to flood was obtained. In order to study the influence of 3D information on flood risk and combine with other multi-source heterogeneous data for integrated analysis, a 3D visualization assessment and analysis method for flood risk, coupled with the projection pursuit-particle swarm optimization algorithm (PP-PSO) was established (3DVAAM-PP-PSO). A case study from Chaohu City, China, was used to demonstrate the method. The results showed that the PP-PSO algorithm can process high-dimensional information and obtain the objective weight of each index. The 3D information from the influenced buildings had an impact on the evaluation results, which needed to be considered. Through the 3D visualization analysis, the overall distribution of flood risk and that around the buildings were obtained in multi-perspectives. The flood risk during different rainfall return periods were analyzed intuitively and comparatively. This study furnishes a novel method for flood risk assessment and analysis by making the most of 3D spatial information.

机译：由于建筑物对洪水分布的影响及其经济和社会属性，3D空间信息，如建筑物的大小和建筑物相对于其高度的洪水比率对城市洪水风险的影响越来越大。但是，现有的洪水风险评估方法主要使用2D中的数据，分析方法主要是2D。在本研究中，通过耦合城市排水模型和3D可视化方法的洪水仿真模型以3D动态可视化形式分析洪水变化过程。通过进一步结合3D建筑模型，获得了与洪水相关的建筑物的3D空间信息。为了研究3D信息对洪水风险的影响，与其他多源异构数据相结合的综合分析，3D可视化评估和洪水风险分析方法，与投影追踪粒子群优化算法相结合（PP-PSO建立了（3DVAAM-PP-PSO）。中国巢湖市案例研究曾经展示了该方法。结果表明，PP-PSO算法可以处理高维信息并获得每个索引的客观权重。受影响的建筑物的3D信息对评估结果产生了影响，这需要考虑。通过3D可视化分析，在多视角获得了洪水风险的总体分布和围绕建筑物。在不同的降雨期间的洪水风险直观地和相对分析。本研究提供了一种新颖的洪水风险评估方法，通过制作大部分3D空间信息来分析。

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来源
《Journal of Environmental Management》 |2020年第15期|110521.1-110521.15|共15页
作者
Guozheng Zhi; Zhenliang Liao; Wenchong Tian; Jiang Wu;
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作者单位

College of Environmental Science and Engineering Tongji University Shanghai 200092 China Shanghai Urban Water Resources Development and Utilization National Engineering Center Co. Ltd. Shanghai 200082 China;

College of Environmental Science and Engineering Tongji University Shanghai 200092 China Xinjiang Branch of National Engineering Research Center for Urban Water Resources Development and Utilization (South) Xinjiang University Urumqi 830047 China Key Laboratory of Yangtze River Water Environment (Ministry of Education) UNEP-Tongji Institute of Environment for Sustainable Development Tongji University Shanghai 200092 China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092 PR China;

College of Environmental Science and Engineering Tongji University Shanghai 200092 China;

MOE Joint Lab for International Cooperation on Eco-Urban Design College of Architecture and Urban Planning Tongji University Shanghai 200092 China;

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收录信息美国《科学引文索引》(SCI);美国《化学文摘》(CA);
原文格式 PDF
正文语种 eng
中图分类
关键词
3D visualization; Multi-source heterogeneous data; Flood risk assessment; PP-PSO algorithm;

机译：3D可视化;多源异构数据;洪水风险评估;PP-PSO算法;

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5. 3D Point Clouds in Urban Planning: Developing and Releasing High-end Methodologies based on LiDAR and UAV Data for the Extraction of Building Parameters [D] . Rebelo, Carla Cristina Roque. 2016

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Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data

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