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相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

267
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:
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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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相关实验视频

Updated: Sep 13, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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同时识别地下水污染源和基于Rime优化算法的模拟模型参数.

Xiao Wang1,2,3, Wenxi Lu4,5,6, Zibo Wang1,2,3

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.

Environmental monitoring and assessment
|August 2, 2025
PubMed
概括

本研究引入了使用一维卷积神经网络 (1DCNN) 和 rime优化算法 (RIME) 的地下水污染源识别 (GCSI) 的新框架. 该方法准确地识别了污染源和含水层参数,改善了整治工作.

关键词:
地下水污染源的识别和识别一维卷积神经网络的一个维度.里姆优化算法 里姆优化算法模拟优化方法的模拟优化方法替代模型的替代模型

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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
  • 水文地质学 水文地质学
  • 机器学习 机器学习

背景情况:

  • 识别地下水污染源 (GCSI) 对于有效的现场整治和责任评估至关重要.
  • 现有的GCSI模拟优化方法在优化中与代用模型准确性和局部最佳情况作斗争.
  • 准确识别污染源特征 (位置,释放历史) 和含水层参数至关重要.

研究的目的:

  • 通过集成先进的机器学习和优化算法,提出地下水污染源识别 (GCSI) 的创新框架.
  • 通过使用一维卷积神经网络 (1DCNN) 作为替代模型来提高GCSI的准确性和稳定性.
  • 验证拟议框架在同时识别污染源特征和含水层参数方面的有效性.

主要方法:

  • 开发了一种新的GCSI框架,将一维卷积神经网络 (1DCNN) 嵌入为集成的替代组件.
  • 使用 rime优化算法 (RIME) 来解决用于同时参数和源识别的复合优化模型.
  • 通过假设的案例研究验证了框架,并将其性能与现有方法进行了比较.

主要成果:

  • 1DCNN替代模型实现了高R平方值0.9998,超过了FCNN和SVR,在±20%的噪音下保持R平方值高于0.9993.
  • RIME算法在PSO,GA和EVO上表现出卓越的性能,单一识别的平均相对误差为8.88%,在100个试验中为5.88%.
  • 拟议的框架成功地实现了污染源特征和含水层参数的同时识别.

结论:

  • 集成的1DCNN和RIME框架为地下水污染源的识别提供了强大而准确的方法.
  • 该方法能够逃避局部最佳并汇聚到全球解决方案的能力使其在复杂的水文地质问题上非常有效.
  • 这种方法在污染物运输和异质含水层表征方面具有更广泛的应用潜力.