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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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Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation and Co-precipitation01:17

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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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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Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

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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: Jan 14, 2026

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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一个物理知情的,独立于领域的数据驱动的洪水预测模型.

Felix Schmid1, Leonie Müller1, Jorge Leandro1

  • 1Department of Civil Engineering, Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany.

Water research
|October 19, 2025
PubMed
概括

这项研究引入了一种新的基于物理信息的数据驱动模型,用于实时预测雨水洪水淹水情况. 该系统在新地区提供准确的空间和时间水深预测,改进了传统方法.

科学领域:

  • 水文和水资源工程 水文和水资源工程
  • 地理空间分析的研究.
  • 环境科学中的人工智能

背景情况:

  • 操作性洪水预测需要准确的空间和时间水深数据,这对于雨季洪水事件期间的公共安全至关重要.
  • 传统的基于物理的模型对于实时预测来说太慢了,而现有的数据驱动模型缺乏域独立性.
  • 目前的数据驱动方法通常需要对较大的采集区进行下调采样,从而限制了它们的适用性.

研究的目的:

  • 开发一个物理知情的数据驱动预测系统,能够实时空间和时间地预测以前未见过的地区的水深洪水.
  • 克服传统和现有的数据驱动洪水预测模型的局限性.
  • 增强数据驱动洪水模型的领域独立性和适用性.

主要方法:

  • 一个采用图像到图像翻译过程的卷积神经网络 (CNN) 被开发出来,从德国 Baiersdorf 培训了采集区特征.
  • 实施了一个时空预测框架,在23个未知的区域中测试了10分钟的时间步进和域独立预测.
  • 整合了物理知情损失函数,结合了2D连续性方程和动力波公式来估计速度和执行物理约束.

主要成果:

  • 该模型在未知区域实现了约74%的临界成功指数 (CSI) 和0.045m的平均根平均平方误差 (RMSE).
  • 与标准的数据驱动损失函数相比,物理知情损失函数表现出更高的性能,将RMSE降低约25%.
关键词:
连续性和动力波浪损失.深度学习是一种深度学习.对未见的水域进行概括.基于物理学的神经网络.时间空间预测.城市洪水城市洪水

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  • 该框架成功提供了具有10分钟时间分辨率的预测,消除了对采样减少的需求.
  • 结论:

    • 拟议的基于物理信息的数据驱动系统提供了一个可行的解决方案,用于在不同地理位置进行实时的雨水洪水淹没地图.
    • 将物理原理集成到数据驱动模型中,可以显著提高洪水预测的预测准确性和可靠性.
    • 开发的框架表明了准确和高效的洪水预测系统的潜力,适用于广泛的水域.