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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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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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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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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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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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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 23, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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使用机器学习预测复合海洋-河流洪水.

Sogol Moradian1, Amir AghaKouchak2, Salem Gharbia3

  • 1College of Science and Engineering, University of Galway, Galway, Ireland; EHIRG EcoHydroInformatics Research Group, University of Galway, Ireland.

Journal of environmental management
|June 14, 2024
PubMed
概括

这项研究引入了一种新的水力动力学机器学习方法,用于预测复合沿海河流洪水. 该系统准确预测洪水淹没和水深,优于早期预警系统的传统方法.

关键词:
人工智能的人工智能是人工智能.复合危险 复合危险是指复合的危险.洪水预测预测 洪水预测河流和海洋的洪水.机器学习是机器学习.

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科学领域:

  • 环境科学 环境科学
  • 水文学的水文学
  • 数据科学数据科学数据科学

背景情况:

  • 传统的洪水评估往往忽略了由多个来源 (海洋,河流,雨水) 驱动的复合事件.
  • 有效的洪水风险管理需要准确预测复杂的,多驱动因素的洪水事件.

研究的目的:

  • 开发和评估一种新的两步框架,用于模拟和预测复合沿海-河流洪水.
  • 将水力动力学模拟与机器学习相结合,以提高洪水预测的准确性和速度.

主要方法:

  • 一个两步框架,将水力动力学模拟用于洪水传播和机器学习 (ML) 模型用于预测.
  • 七个ML模型 (SVR,SVM,RBF,LR,GPR,DT,ANN) 使用河流排水和海洋水位数据进行了每像素的训练.
  • 该系统在爱尔兰科克市用于复合沿海和河流洪水预测,并经过验证.

主要成果:

  • 结合的水力动力学-ML方法提供了可靠的洪水淹没和水深的估计.
  • 辐射基函数 (RBF) 模型在测试的ML模型中表现最好.
  • 该系统成功地用有限的输入数据预测了沿海和河流洪水,克服了传统模型的计算局限性.

结论:

  • 开发的系统为短期洪水预测提供了传统水力动力学模型的计算效率高的替代方案.
  • 这种方法可以实现近乎实时的洪水预测,适合集成到预警系统中.
  • 准确的复合洪水预测具有显著的社会效益,包括更好的准备和减少洪水损害.