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相关概念视频

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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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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Conservation of Mass in Moving, Nondeforming Control Volume01:14

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Stormwater detention basins are essential in managing runoff during heavy rainfall, particularly in urban areas where impervious surfaces increase the risk of flooding. Understanding the conservation of mass in these systems allows engineers to optimize basin performance, balancing inflow, outflow, and water storage.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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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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相关实验视频

Updated: Jun 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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使用分解优化算法和机器学习方法预测矿井水流量.

Jiaxin Bian1,2,3, Tao Hou4, Dengjun Ren4

  • 1School of Water and Environment, Chang'an University, Xi'an, 710064, China.

Scientific reports
|August 1, 2024
PubMed
概括

一个结合CEEMDAN,NGO和LSTM的新模型准确地预测了突然的矿井水流量变化. 这种先进的方法改善了智能矿山的安全监控.

关键词:
在CEEMDAN,你会发现.深度学习模型深度学习模型这是LSTM的LSTM.矿井的水流入,使矿井的水流入.无政府组织 NGO NGO 无政府组织短期预测 短期预测

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科学领域:

  • 地质科学和环境科学 地球科学和环境科学
  • 人工智能和机器学习
  • 采矿工程 采矿工程 采矿工程

背景情况:

  • 矿井水的流入对煤矿安全构成重大风险,特别是在深度操作中.
  • 由于复杂的水文地质参数和传统模型的局限性,准确预测突然的水流入量变化是具有挑战性的.
  • 现有的单一机器学习方法难以预测矿井水流量突然变化的情况.

研究的目的:

  • 开发和评估一种新的合分解-优化-深度学习模型,用于增强矿井水流入预测.
  • 为了比较单一,分解预测和分解优化预测合模型在捕捉突然变化的性能.
  • 为智能矿山安全监控提供强大的技术解决方案.

主要方法:

  • 整合完整的集体实证模式分解与自适应噪声 (CEEMDAN),北方Goshawk优化 (NGO) 和长短期内存 (LSTM) 网络.
  • 评估了三种预测方法:单一时间序列预测,CEEMDAN-LSTM和CEEMDAN-NGO-LSTM.
  • 评估预测准确度和捕捉矿井水流量数据突然变化的能力.

主要成果:

  • 在预测极端变化方面,CEEMDAN-NGO-LSTM模型显著优于单一预测和CEEMDAN-LSTM模型.
  • 在CEEMDAN-NGO-LSTM模型中,MAE达到了96.578,MAPE达到了1.471%,RMSE达到了122.143,NSE达到了0.958.
  • 这种合模型的平均性能比LSTM和CEEMDAN-LSTM分别提高了44.950%和19.400%.
  • 该模型提供了最准确的5天前的矿井水流量预测.

结论:

  • 拟议的CEEMDAN-NGO-LSTM模型为预测矿井水流入,特别是突然变化的预测提供了一种卓越的方法.
  • 这种分解-优化-预测相结合的模型增强了智能采矿环境中的安全监控能力.
  • 该研究为确保安全和高效的煤炭开采业务提供了重要的理论和实践价值.