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

Gradually Varying Flow01:29

Gradually Varying Flow

88
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...
88
Rapidly Varying Flow01:24

Rapidly Varying Flow

98
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...
98
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

90
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...
90
Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

140
Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
140
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

72
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...
72

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

Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm.

Environmental monitoring and assessment·2025
Same author

Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.

Scientific reports·2025
Same author

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

Water science and technology : a journal of the International Association on Water Pollution Research·2024
Same author

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Environmental monitoring and assessment·2024
Same author

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Environmental monitoring and assessment·2024
Same author

Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Environmental monitoring and assessment·2023

相关实验视频

Updated: Jul 23, 2025

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events
06:26

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

Published on: November 7, 2017

16.3K

新型人工蜂群优化ANN和数据预处理技术的应用,用于每月流量估计.

Okan Mert Katipoğlu1, Mehdi Keblouti2, Babak Mohammadi3

  • 1Erzincan Binali Yıldırım University, Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan, Türkiye. okatipoglu@erzincan.edu.tr.

Environmental science and pollution research international
|July 17, 2023
PubMed
概括

精确的流量估计对于水资源管理至关重要. 这项研究引入了新的混合模型,将人工蜂群优化的人工神经网络与信号分解技术相结合,以改善水文预测.

关键词:
人工蜜蜂群的优化 蜂群的优化东黑海地区 东黑海地区经验模式分解分解当地平均分解分解流量预测预测流量预测.

更多相关视频

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.3K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

相关实验视频

Last Updated: Jul 23, 2025

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events
06:26

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

Published on: November 7, 2017

16.3K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.3K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

科学领域:

  • 水文和水资源管理 水文和水资源管理
  • 环境科学中的计算智能

背景情况:

  • 准确的流量估计对于可持续的水资源管理,防灾和各种与水有关的应用至关重要.
  • 传统的水文模型往往在准确预测河流流量方面面临挑战,特别是在容易发生干旱和洪水等极端事件的地区.

研究的目的:

  • 开发和评估用于增强流量估计的新型混合模型.
  • 评估结合人工蜂群 (ABC) 优化的人工神经网络 (ANN) 与先进的信号分解技术的有效性.

主要方法:

  • 开发了一个人工蜂群-人工神经网络 (ABC-ANN) 混合模型.
  • 集成了ABC-ANN模型与局部平均分解 (LMD) 和完整集体实证模式分解与自适应噪声 (CEEMDAN) 信号分解技术.
  • 应用这些混合模型 (LMD-ABC-ANN和CEEMDAN-ABC-ANN) 在东黑海地区,土耳其进行流量预测.

主要成果:

  • 该研究成功评估了新的LMD-ABC-ANN和CEEMDAN-ABC-ANN混合方法的性能.
  • 证明了这些先进的混合模型在提高流量预测准确度方面的潜力.

结论:

  • 开发的混合模型为增强流量估计提供了可靠的策略.
  • 这些发现为水资源规划者和政策制定者提供了有价值的资源,帮助他们有效地管理水资源.