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相关概念视频

Typical Model Studies01:30

Typical Model Studies

376
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.
376
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

63
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...
63
Modeling and Similitude01:12

Modeling and Similitude

284
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...
284
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Response Surface Methodology01:16

Response Surface Methodology

171
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
171
Sampling Plans01:23

Sampling Plans

205
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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相关实验视频

Updated: Jul 15, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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一个集成的建模框架,用于在地表水中识别多重污染源.

Xiaodong Liu1, Xuneng Tong2, Lei Wu3

  • 1Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Yangtze Institute for Conservation and Development, Hohai University, Jiangsu 210098, China.

Journal of environmental management
|October 1, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用人工神经网络和粒子群优化的混合模型,用于准确识别水体中的污染源. 开发的框架有效地确定了污染源,并量化了排放强度,加强了水安全管理.

关键词:
反向繁殖是一种反向传播.一般回归法一般回归法粒子群集优化优化 粒子群集优化污染源的识别和识别射线基础函数的作用替代模型的建模

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相关实验视频

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科学领域:

  • 环境科学 环境科学
  • 水资源管理 水资源管理
  • 计算建模 计算建模

背景情况:

  • 识别污染源对于有效的水安全管理至关重要.
  • 现有的方法可能缺乏复杂水系统所需的速度,准确性或可靠性.
  • 综合建模框架为应对这些挑战提供了一个有希望的方法.

研究的目的:

  • 开发和评估一个集成的模拟优化建模框架,以快速,准确和可靠地识别污染源.
  • 测试框架在稳定 (流水) 和不稳定 (长江河口) 流量条件下的有效性.
  • 在框架内比较不同人工神经网络 (ANN) 算法的性能.

主要方法:

  • 开发基于过程的模型 (PBM) 来模拟水力动力学和水质.
  • 使用PBM生成的数据创建了人工神经网络 (ANN) 替代模型 (BP,RBF,GRNN).
  • 将PBM-ANNs与粒子群优化 (PSO) 结合起来,形成一个混合建模框架.
  • 使用实验室流水和现实生活河口数据验证了框架.

主要成果:

  • 混合PBM-ANNs-PSO模型成功地确定了污染源,并量化了连续释放点源的排放强度.
  • 使用多个标准 (R2,RMSE,MAE) 评估模型性能为"优秀预测".
  • 与PSO (BP-PSO) 相结合的反向传播-ANNs表现出优异的性能,相对误差通常低于5%.

结论:

  • 综合模拟优化框架为识别污染源和量化污染强度提供了强大的工具.
  • BP-PSO模型是水系统中污染源识别的高效方法.
  • 调查结果为地方政府机构在做出明智的污染控制决策时提供了宝贵的见解.