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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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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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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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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Gradually Varying Flow01:29

Gradually Varying Flow

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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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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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相关实验视频

Updated: Jul 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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使用计算智能方法进行长期预测,同时考虑不确定性问题.

Mohammad Najafzadeh1, Sedigheh Anvari2

  • 1Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, P.O. Box 76315117, Kerman, Iran. moha.najafzadeh@gmail.com.

Environmental science and pollution research international
|June 27, 2023
PubMed
概括

本研究量化了用于流量预测的人工智能 (AI) 模型中的不确定性. 与多变量自适应回归分线 (MARS) 和基因表达编程 (GEP) 相比,模型树 (MT) 的不确定性较低.

关键词:
人工智能模型的人工智能模型统计措施 统计措施流量预测流量预测不确定性分析不确定性分析

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科学领域:

  • 水文与水资源工程 水文与水资源工程
  • 环境科学中的人工智能
  • 用数据驱动的水资源管理建模.

背景情况:

  • 人工智能 (AI) 技术,如基因表达编程 (GEP),模型树 (MT) 和多变量自适应回归线 (MARS) 越来越多地用于水资源.
  • 对这些人工智能模型的不确定性水平缺乏研究,这对于可靠的流量预测至关重要.
  • 准确的流量预测对于防止水资源管理不善的影响至关重要.

研究的目的:

  • 在流量预测中调查和量化与GEP,MT和MARS模型相关的不确定性.
  • 使用全球每日流量数据集,比较这三种人工智能技术的不确定性水平.
  • 评估这些模型对于实际流量预测应用的适用性.

主要方法:

  • 利用全球日流数据集进行模型培训和验证.
  • 使用基因表达编程 (GEP),模型树 (MT) 和多变量自适应回归线 (MARS) 进行流量预测.
  • 使用95%百分比预测不确定性 (95%PPU) 和R因子统计指标量化模型不确定性.

主要成果:

  • 模型树 (MT) 的不确定性最低,95%PPU为0.59,R因子为1.67.
  • 多变量自适应回归线 (MARS) 的不确定性略高 (95%PPU=0.61,R因子=1.92).
  • 在测试模型中,基因表达编程 (GEP) 的不确定性最高 (95%PPU=0.64,R-factor=2.03).
  • 虽然不确定性带通常捕获了平均流量测量,但宽带表明每月流量预测的不确定性很大.

结论:

  • 模型树 (MT) 是一种更可靠的AI技术,用于流量预测,因为它的不确定性更低.
  • 尽管捕获了平均值,但广泛的不确定性波段强调了在使用这些人工智能模型进行关键水资源管理决策时需要谨慎.
  • 需要进一步的研究来减少基于AI的流量预测模型的不确定性.