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基于人工神经网络模型的铜海水质量标准的推导

Yang Li¹, Di Mu¹, Hong-Qing Wu¹

¹Engineering Research Center of Seawater Utilization of Ministry of Education, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin, 300401, China; Hebei Collaborative Innovation Center of Modern Marine Chemical Technology, Tianjin, 300401, China.

Environmental pollution (Barking, Essex : 1987)

|October 23, 2024

概括

一个新的基因算法优化的反向传播神经网络 (BPNN) 模型通过减少物种特异性影响来改善水质标准 (WQC) 铜毒性预测. 这种先进的模型在海洋环境中提供了更准确的WQC导出.

科学领域:

环境科学环境科学
生态毒理学生态毒理学
计算化学计算化学

背景情况:

铜 (Cu) 对水的化学作用对于建立水质标准 (WQC) 至关重要.
当前的多重回归模型在与特定物种的变化作斗争,影响Cu毒性预测的准确性.
准确预测Cu的毒性对于有效的环境风险评估和管理至关重要.

研究的目的:

开发和验证一种基因算法优化的反向传播神经网络 (BPNN) 模型,用于预测Cu的毒性.
解决和减轻传统回归模型在计算物种特异效应方面的局限性.
为了在海洋环境中获得更可靠的铜WQC.

主要方法:

开发一个基因算法优化的反向传播神经网络 (BPNN) 模型.
比较BPNN模型与传统的多重线性回归 (MNLR) 模型,使用聚合和特定物种数据.
应用经过验证的模型来推导各种环境条件下的铜的短期WQC.

主要成果:

与大多数分类组的MNLR模型相比,BPNN混合模型显示出更高的预测性能 (Adj.R2).
即使使用特定物种数据,BPNN模型也保持了卓越的预测准确度.
BPNN模型有效地减少了物种特异性影响的影响,通过对M. galloprovincialis的分析得到了验证.

结论:

优化的BPNN模型在预测铜毒性方面提供了更高的准确性,优于传统的回归方法.
BPNN模型成功考虑了特定物种的变异,从而导致更强大的WQC衍生.
使用BPNN模型推导的WQC范围 (1.6-4.41μg/L) 与全球指导一致,证实了其实际可行性.

关键词:

环境因素环境因素多重回归是一种多重回归.神经网络的神经网络的神经网络海水质量标准海水质量标准特定物种的影响.

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