基于机器学习方法的小麦镉富集因子预测

应用机器学习方法解析区域土壤-小麦系统镉(Cd)富集特征有助于风险决策的准确性和科学性.基于区域调查，构建了Freundlich-type转移方程、随机森林(RF)模型和神经网络(BPNN)模型对小麦Cd富集因子(BCF-Cd)进行预测，验证不同模型的预测精度并评估其不确定性.结果表明，RF(R²=0.583)和BPNN(R²=0.490)模型预测性能均优于Freundlich转移方程(R²=0.410).重复训练结果显示RF和BPNN平均绝对误差(MAE)和均方根误差(RMSE)较为接近，但RF(R²为0.527～0.601)较BPNN(R²为0.432～0.661)模型精度和稳定性更高.特征变量重要性分析显示多重因素的共同作用导致小麦BCF-Cd的异质性，其中土壤磷(P)和锌(Zn)是影响小麦BCF-Cd变化的关键变量.参数优化可进一步提高模型精度、稳定性和泛化能力.

Prediction of Cadmium Uptake Factor in Wheat Based on Machine Learning

Applying machine learning methods to resolve the cadmium (Cd) uptake characteristics of regional soil-wheat systems can contribute to the accuracy and rationality of risk decisions. Based on a regional survey, we constructed a Freundlich-type transfer equation, random forest (RF) model, and neural network (BPNN) model to predict wheat Cd enrichment factor (BCF-Cd); verified the prediction accuracy; and assessed the uncertainty of different models. The results showed that both RF (R²=0.583) and BPNN (R²=0.490) were better than the Freundlich transfer equation (R²=0.410). The RF and BPNN were further trained repeatedly, and the results showed that the mean absolute error (MAE) and root mean square error (RMSE) of RF and BPNN were close to each other. Additionally, the accuracy and stability of RF (R²=0.527-0.601) was higher than that of BPNN (R²=0.432-0.661). Feature importance analysis showed that multiple factors led to the heterogeneity of wheat BCF-Cd, in which soil phosphorus (P) and zinc (Zn) were the key variables affecting the change in wheat BCF-Cd. Parameter optimization can further improve the accuracy, stability, and generalization ability of the model.