人工神经网络和多重线性回归的比较,以预测大米中的度:中国广西的一项现场研究
Junyang Zhao1, Fuhai Zheng2, Baoshan Yu1
1Guangxi Key Laboratory of Agro-Environment and Agric-Products Safety, College of Agriculture, Guangxi University, Nanning 530004, China.
Toxics
|August 27, 2025
概括
我们开发了两种模型来预测大米中的 (Cd). 反向传播人工神经网络 (BP-ANN) 模型在预测大米含量方面表现比多重线性回归 (MLR) 模型更好.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 土壤科学 土壤科学
背景情况:
- 土壤-大米系统中的 (Cd) 转移是复杂的,限制了当前土壤-植物模型的有效性.
- 准确预测大米中的Cd含量对于食品安全和风险评估至关重要.
研究的目的:
- 为了比较反向传播人工神经网络 (BP-ANN) 模型和多重线性回归 (MLR) 模型的预测性能,用于估计米粒中的含量.
- 确定影响大米中积累的关键土壤参数.
主要方法:
- 利用486个配对的土壤和大米谷物样本进行培训和验证,以及另外30个来自广西省的样本进行测试.
- 使用土壤可用 (ACd),土壤总 (TCd),土壤有机物 (SOM) 和pH作为预测变量.
- 使用根平均平方误差 (RMSE),相对百分比差异 (RPD) 和相关系数 (R2) 评估模型性能.
主要成果:
- 使用pH,TCd和ACd的MLR模型,实现了0.551的R2,2.398的RPD和0.049的米的RMSE (RCd).
- 使用相同变量的BP-ANN模型,给出了R2的0.6846和RMSE的0.104的RCd.
- 与MLR模型相比,BP-ANN模型显示出更高的预测准确性.
结论:
- 反向传播人工神经网络 (BP-ANN) 模型对预测米中的含量是有效的.
- 与传统的多重线性回归 (MLR) 相比,BP-ANN为土壤-大米系统中的预测提供了更好的性能.
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