Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Typical Model Studies01:30

Typical Model Studies

340
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.
340
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

127
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.
127
Rapidly Varying Flow01:24

Rapidly Varying Flow

53
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...
53
Modeling and Similitude01:12

Modeling and Similitude

246
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...
246
Gradually Varying Flow01:29

Gradually Varying Flow

34
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...
34
Plane Potential Flows01:23

Plane Potential Flows

369
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
369

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hydrodynamics and water quality of a highly anthropized wetland: the case study of the Massaciuccoli basin (Tuscany, Italy).

Environmental science and pollution research international·2024
Same author

High-resolution spatial analysis of temperature influence on the rainfall regime and extreme precipitation events in north-central Italy.

The Science of the total environment·2023
Same author

Deep learning models to predict flood events in fast-flowing watersheds.

The Science of the total environment·2021
Same author

Beyond one-way determinism: San Frediano's miracle and climate change in Central and Northern Italy in late antiquity.

Climatic change·2021
Same author

Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images.

Marine pollution bulletin·2021

相关实验视频

Updated: Jun 8, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K

机器学习模型用于预测小流域的河流流量.

Marco Luppichini1, Giada Vailati2, Lorenzo Fontana3

  • 1Department of Earth Sciences, University of Pisa, Via S. Maria, 52, 56126, Pisa, Italy. marco.luppichini@dst.unipi.it.

Scientific reports
|November 5, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型为快速响应的河流流域提供准确的水文高度预测,有助于气候变化适应. 这些工具为水文地质风险提供了至关重要的早期警告,使得及时的缓解措施成为可能.

关键词:
深度学习是一种深度学习.意大利 意大利 意大利 意大利降低风险 降低风险预测河流流动的预测

更多相关视频

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.2K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.8K

相关实验视频

Last Updated: Jun 8, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.2K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.8K

科学领域:

  • 环境科学环境科学
  • 水文学的水文学
  • 数据科学是数据科学.

背景情况:

  • 气候变化需要先进的工具来减轻水文地质风险.
  • 小水域特别容易受到快速水文变化的影响.
  • 有效的预测对于地方当局及时管理风险至关重要.

研究的目的:

  • 研究深度学习模型,用于预测快速水文盆地的水度高度.
  • 评估这些模型用于预测极端事件的准确性和交付时间.
  • 评估结合信心区间来提高预测准确性的好处.

主要方法:

  • 应用深度学习模型来预测水文高度.
  • 测试各种输入数据集以优化模型性能.
  • 整合机器学习模型用于预测置信区间.
  • 使用诸如根平均平方误差 (RMSE) 等指标评估模型性能.

主要成果:

  • 深度学习模型在几个小时的预测中实现了非常小的错误 (厘米).
  • 通过 4-6 小时的预测时间 (RMSE 10-30 厘米) 证明了极端事件的准确预测.
  • 结合信心区间,可以在较长的预测期内提高预测准确度.
  • 结合不同的模型,提供了对河流演变的更全面的观点.

结论:

  • 深度学习模型是预测快速响应盆地的河流流量的有效工具.
  • 这些模型使客观和快速的应用成为可能,促进了用户友好的软件的开发.
  • 该技术支持地方当局为减轻水地风险做出明智决策.