使用GA-BP神经网络模型对土壤重金属污染的定量逆转
Yi-Ming Chen1, Zhe Wang2, Chao-Liang Peng1
1College of Environment and Resources, Southwest University of Science & Technology, Mianyang, 621010, Sichuan, China.
Environmental monitoring and assessment
|October 14, 2025
概括
准确的重金属土壤污染评估至关重要. 一个基因算法优化神经网络 (GA-BPNN) 模型有效量化土壤中的 (Pb) 和 (Cd),帮助环境修复工作.
科学领域:
- 环境科学 环境科学
- 土壤科学 土壤科学
- 地质化学 地质化学
背景情况:
- 中国的工业化对土壤环境构成重大威胁,特别是重金属污染.
- 重金属度的快速定量逆转和污染风险评估是关键需求.
- 非铁金属炼废渣厂被确定为主要的污染源.
研究的目的:
- 开发准确的模型,用于测量土壤中重金属 (Pb和Cd) 度的定量逆转.
- 评估土壤物理化学特性与重金属含量之间的关系.
- 为重金属污染评估和补救提供一种新的方法.
主要方法:
- 采集和分析农田土壤样本的物理化学特性和Pb,Cd含量.
- 开发使用多重线性回归 (MLR),反向传播神经网络 (BPNN) 和基因算法优化 BPNN (GA-BPNN) 的定量倒置模型.
- 使用R平方和根平均平方误差 (RMSE) 评估模型准确性.
主要成果:
- 该GA-BPNN模型实现了Pb和Cd反转的高精度 (R2分别为0.8980和0.9013).
- 较低的RMSE值 (Pb为0.0001868,Cd为0.0001821) 表示精确的反转.
- 在重金属和土壤物理化学性质之间建立了非线性关系.
结论:
- 该GA-BPNN模型提供了一个高度准确和新的方法来逆转土壤中的重金属含量.
- 这种方法有助于有效评估重金属污染.
- 这些发现对于指导土壤修复策略具有实际价值.
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