在地下水污染源识别中,基于变化模式分解的观测数据无声化的应用
Zibo Wang1, Wenxi Lu1, Zhenbo Chang2
1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Water Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China.
The Science of the total environment
|June 30, 2024
概括
识别地下水污染源需要有效的拒绝. 变量模式分解 (VMD) 是有前途的,其有效性取决于噪声水平和观察频率,当与集体决策优化算法相结合时,提高准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 信号处理 信号处理
背景情况:
- 地下水污染源识别 (GPSR) 对于补救和风险评估至关重要.
- 在GPSR中观察到的数据可能会很,影响识别准确性.
- 现有的否定方法难以处理复杂,非线性和非静止数据,缺乏全面的适用性分析.
研究的目的:
- 在GPSR中引入变化模式分解 (VMD) 进行denoising.
- 通过考虑噪声和观察到的数据属性,全面分析无声化的适用性.
- 通过使用一种新的集体决策优化算法来提高GPSR准确性.
主要方法:
- 应用变化模式分解 (VMD) 用于在GPSR中拒绝噪音观察数据.
- 调查了噪声水平和观察频率对12种场景的无声效果的影响.
- 整合了集体决策优化算法,以改善四个代表性场景的识别准确性.
主要成果:
- 在各种场景中,VMD表现出有效的拒绝.
- 随着噪声水平的增加和观察到的频率的减少,denoising的有效性下降.
- 在高噪音和多个观察到的频率下,Denoising对于GPSR更有效.
- 集体决策优化算法表现出良好的反转精度和稳定性.
结论:
- VMD是一种适用于GPSR的无声化方法,其性能受数据和噪声特征的影响.
- 对无声化适用性的全面分析需要考虑噪声和观察到的数据属性.
- 集体决策优化算法提供了一个强大的方法来提高GPSR准确性.
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