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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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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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Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

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To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
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Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

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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...
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Typical Model Studies01:30

Typical Model Studies

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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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Underflow Gates01:30

Underflow Gates

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Underflow gates are vital for controlling water flow in irrigation canals. The three main types of underflow gates — vertical, radial, and drum gates — serve different purposes while ensuring effective flow management. Vertical gates move up and down, generating a free-flowing water jet; radial gates pivot to regulate the flow; and drum gates rotate for precise adjustments. The flow through these gates is influenced by downstream conditions, resulting in free or drowned outflow.Free and...
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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
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探索机器学习算法,以准确预测马来西亚马达河的水位.

Muhamad Nur Adli Zakaria1, Ali Najah Ahmed1,2, Marlinda Abdul Malek3

  • 1Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia.

Heliyon
|July 17, 2023
PubMed
概括

准确的水位预测对于洪水预警至关重要. 多层感知神经网络 (MLP-NN) 在预测河流水位方面表现最好,优于其他机器学习模型.

关键词:
这是LSTM的LSTM.在MLP中,MLP是MLP.机器学习是机器学习.马来西亚 马来西亚 马来西亚马达河 马达河是马达河的一条河流.水平 水平 水平 水平 水平在XGBoost上使用.

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科学领域:

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

背景情况:

  • 准确的水位预测对于有效的洪水预警系统和可持续的淡水资源管理至关重要.
  • 预测河流和湖泊的水位需要强大的建模技术来处理复杂的水文动态.

研究的目的:

  • 开发和比较三种机器学习算法的性能,用于马来西亚马达河的水位预测.
  • 评估气象数据整合和不同时间范围对模型准确性的影响.

主要方法:

  • 应用多层感知神经网络 (MLP-NN),长期短期记忆神经网络 (LSTM) 和极端梯度增强 (XGBoost) 模型.
  • 利用2016年至2018年的每日水位和气象数据进行模型开发和测试.
  • 通过使用准确度评分来评估模型性能,并在不同的时间范围内评估预测能力.

主要成果:

  • MLP-NN模型实现了最高的整体精度 (0.871),超过了LSTM (0.865) 和XGBoost (0.831).
  • 纳入气象数据并没有显著提高任何测试模型的预测准确性.
  • LSTM模型在7天前的预测中表现出卓越的性能,突出了它在捕捉长期依赖方面的优势.

结论:

  • 每个机器学习模型都有独特的优缺点,性能高度依赖于数据的数量和质量.
  • 对于短期的水位预测,MLP-NN是有效的,而LSTM在长期预测方面表现出色.
  • 对于水位预测的机器学习模型的选择应根据具体的项目要求和数据可用性进行量身定制.