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相关概念视频

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Responses to Drought and Flooding02:41

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Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
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Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
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相关实验视频

Updated: Jun 25, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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基于多个数据驱动模型的城市水浸感受性评估的比较研究.

Feifei Han1, Jingshan Yu2, Guihuan Zhou1

  • 1College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China.

Journal of environmental management
|May 23, 2024
PubMed
概括

这项研究比较了四个数据驱动的模型,用于北京的城市淹水易感性. 微粒子集群优化-弱标记支持向量机 (PSO-WELLSVM) 显示出最佳性能,而建筑密度和暴雨频率是关键因素.

关键词:
这是一个 DISO DISO.机器学习是机器学习.马克斯 恩特 马克斯 恩特不确定性分析不确定性分析城市淹水易受影响的易受性.

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相关实验视频

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科学领域:

  • 环境科学 环境科学
  • 城市规划 城市规划
  • 地理信息系统 地理信息系统

背景情况:

  • 城市淹水问题越来越令人担忧,需要准确的预测和易感性评估.
  • 数据驱动模型提供了复杂的机械模型的替代方案,包括社会经济因素.
  • 现有的研究往往缺乏全面的模型比较和可解释性分析.

研究的目的:

  • 为了比较四个数据驱动模型的性能,以绘制城市淹水易受性的地图.
  • 分析这些模型的可解释性,并确定关键影响因素.
  • 开发一种综合方法来减少预测的不确定性.

主要方法:

  • 构建了四种模型:随机森林 (RF),带有辐射基函数的支持向量机 (SVM-RBF),粒子群优化弱标记的支持向量机 (PSO-WELLSVM) 和最大 (MaxEnt).
  • 用12个解释变量来预测北京市中心地区的浸水易感.
  • 模拟和观察指数之间的距离 (DISO) 用于全面的模型性能评估,并使用地理探测器进行可解释性分析.

主要成果:

  • PSO-WELLSVM显示了最高的性能 (DISOtest = 0.63),超过了MaxEnt (DISOtest = 0.78) 的表现.
  • 马克森特在识别高度敏感区域方面表现出色,而RF和SVM-RBF显示出低于最佳的性能和过度装配.
  • 建筑密度 (BD) 是最有影响力的因素,其次是距离道路的距离和暴雨频率 (FHR). 像BD和FHR这样的因素之间的相互作用,非线性地增加了易感性预测能力.

结论:

  • 这项研究强调了PSO-WELLSVM在浸水易感测绘方面的有效性,以及考虑多个因素的重要性.
  • 与单个模型相比,集成多个模型显著降低了预测不确定性.
  • 了解建筑密度和大雨等因素的相互作用,对于有效的城市淹水风险管理至关重要.