土壤2025，Vol.57Issue(2)：423-429,7.DOI:10.13758/j.cnki.tr.2025.02.021
基于辅助变量和GARBF神经网络的黄河流域土壤镉空间分布预测
Prediction of Spatial Distribution of Soil Cd in Yellow River Basin Based on Auxiliary Variables and GARBF Neural Network
摘要
Abstract
In order to accurately grasp the spatial distribution of soil cadmium in the Yellow River Basin,different combinations of environmental factors and soil physicochemical factors were used as auxiliary variables,and the genetic algorithm(GA)was used to optimize the radial basis function(RBF)neural network to predict the spatial distribution of soil cadmium in the Yellow River Basin,and the prediction accuracy of this model was compared with those of the regression Kriging and the RBF neural network,to investigate the effects of soil physicochemical factors and GA on the prediction accuracy of the neural network.The results showed that:1)The addition of soil physicochemical factors(organic matter content,pH,CEC)could improve the prediction accuracy of the neural network model.The root mean square error(RMSE),mean absolute error(MAE),and mean relative error(MRE)of the GARBF neural network model based on the environmental factors and soil physicochemical factors were reduced by 0.058 mg/kg,0.033 mg/kg,and 4.4 percentage points,respectively;2)GA could improve the prediction accuracy of neural network models,and the RMSE,MAE,and MRE of the GARBF neural network model based on environmental factors and soil physicochemical factors were reduced by 0.009 mg/kg,0.005 mg/kg,and 0.6 percentage points,respectively,compared with the RBF neural network model based on environmental factors and soil physicochemical factors.3)The prediction results obtained by adding environmental factors and soil physicochemical factors and optimizing the neural network model using GA were optimal,and the GARBF neural network model based on environmental factors and soil physicochemical factors could be used to predict the spatial distribution of soil cadmium in the Yellow River Basin.关键词
土壤理化因子/遗传算法/神经网络/辅助变量/空间插值Key words
Soil physiochemical factors/Genetic algorithm/Neural networks/Auxiliary variables/Spatial interpolation分类
资源环境引用本文复制引用
张成才,郑文豪,闫亚宁,孙雨田,刘威,王永辉..基于辅助变量和GARBF神经网络的黄河流域土壤镉空间分布预测[J].土壤,2025,57(2):423-429,7.基金项目
中央水专项资金项目(20220086A)和河南省自然科学基金项目(222300420539)资助. （20220086A）
