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多二-p-二氧化物和多二 furan (PCDD/Fs) 预测模型基于有限的泥炭样本,使用进化的人工神经网络进行预测

Shir Li Wang¹, Theam Foo Ng², Khairulmazidah Mohamed³

¹Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900, Tanjong Malim, Perak, Malaysia; Data Intelligent and Knowledge Management (DILIGENT), Universiti Pendidikan Sultan Idris, 35900, Tanjong Malim, Perak, Malaysia.

|June 22, 2024

概括

这项研究预测了利用优化的人工神经网络 (ANN) 在泥炭土中排放有毒的多二子-p-二氧化物和二子 (PCDD/Fs). 进化优化的ANN准确地预测PCDD/F,帮助控制污染和环境监测.

科学领域:

环境化学环境化学
计算毒理学计算毒理学
机器学习应用机器学习应用

背景情况:

多二二-p-二氧化物和二酸 (PCDD/Fs) 是不完全燃烧的高度有毒的副产品.
马来西亚的泥炭土,通常是酸性和高含量,可以促进PCDD/Fs的形成.
准确预测PCDD/Fs排放对于环境保护和公共卫生至关重要.

研究的目的:

开发和优化人工神经网络 (ANN) 模型,用于预测泥炭土中PCDD/Fs排放.
通过使用差异演化 (DE) 优化初始连接权重和偏差来提高ANN预测准确度.
评估一种进化优化的ANN方法论的可行性,用于PCDD/Fs.的环境监测.

主要方法:

使用多层感知子 (MLP) 与反向传播算法用于PCDD/Fs预测.
采用差异进化 (DE),特别是基于自我适应的集群的差异进化与增强的人口大小 (SAEDE-EP),以优化ANN参数.
训练并测试了各种ANN架构,使用现实世界的数据集与八个输入变量和一个输出变量.

主要成果:

优化的ANN,包括5个隐藏的神经元和SAEDE-EP,实现了最低的测试平均平方误差 (MSE_test) 6.1790 × 10−3.
该模型表现出高预测准确度,由0.97447.7的最高R2值证明.
进化优化的ANN方法在预测PCDD/Fs排放方面被证明是有效的.

结论:

一种以进化优化的ANN为基础的方法是预测泥炭土中PCDD/Fs排放的成本高效和可行的解决方案.
这种方法可以显著帮助污染控制,环境监测和当局防止人类接触PCDD/Fs.
该研究强调了先进的计算方法在解决环境污染问题的潜力.

关键词:

适应性参数适应性参数人工神经网络的人工神经网络不同进化的差异进化.皮特兰德是一个泥炭地.多化二子-p-二氧化物和多化二酸 (PCDD/Fs)

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