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自适应进化模型下的土壤重金属含量预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对 Elman 神经网络在土壤重金属含量预测时出现预测精度低、模型收敛速度慢等问题,提出一种自适应进化模型(AEM).该模型以Elman神经网络为基础,运用贝叶斯正则化优化Elman神经网络的目标函数,提高网络模型预测精度;为解决网络模型收敛速度慢和易陷入局部极值等缺陷,采用自适应灰狼算法(AGWA)对网络模型初始参数进行优化;采用基于熵权距离的离群点检测法剔除数据中的离群点,以降低离群点对预测结果的干扰.以武汉市农业科学院采集的农田土壤重金属含量数据进行预测试验,AEM 模型预测重金属含量的平均绝对误差和平均绝对百分比误差分别为 1.623和 17.48%,其决定系数比Elman的提高了 0.394.AEM、自调整反距离加权插值模型(SIDIM)、小波神经网络模型(CBSA-WNN)、双向门控循环神经网络模型(SBGRNN)及Elman神经网络模型等 5 种不同预测模型进行对比试验表明,AEM模型在土壤重金属含量预测上具有更高的准确性.消融试验结果表明,贝叶斯正则化优化、自适应灰狼算法优化和基于熵权距离的离群点检测的离群点数据剔除等 3个改进点对于提升土壤重金属含量预测精度均有一定的贡献.
Soil heavy metal content under adaptive evolution model
Aiming to address the problem of low prediction accuracy and slow convergence speed of Elman neural networks in predicting soil heavy metal content,an adaptive evolutionary model was proposed in this study.This model was based on the Elman neural network and used Bayesian regularization to optimize the objective function of the Elman neural network,improving the prediction accuracy of the network model.To overcome the shortcomings of slow convergence speed and susceptibility to local extremum in network models,the Adaptive Gray Wolf algorithm(AGWA)was used to optimize the initial parameters of the network model.And an outlier detection method based on entropy weight distance was used to remove outliers from the data,in order to reduce the interference of outliers on the prediction results.The prediction experiment was conducted using data on heavy metal content in farmland soil collected by Wuhan Academy of Agricultural Sciences.The average absolute error and average absolute percentage error of AEM model for predicting heavy metal content were 1.623 and 17.48%,respectively.Compared with Elman's comparative model,the determination coefficient index improved by 0.394.After conducting comparative experiments with five different prediction models(AEM,Elman,SBGRNN,SIDIM,CBSA-WNN),it was found that the AEM model had the highest accuracy in predicting soil heavy metal content.The results of the ablation experiment indicated that the three improvement points(Bayesian regularization optimizing,AGWA optimizing,and removing outliers from the data by using an outlier detection method based on entropy weight distance)all contributed to improving the accuracy of predicting soil heavy metal content to varying degrees.

soil heavy metaladaptive evolution modeGrey Wolf algorithmElman neural networkdata prediction

李亮亮、张聪、曹坤、黎帅锋

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武汉轻工大学数学与计算机学院,湖北 武汉 430023

武汉轻工大学电气与电子工程学院,湖北 武汉 430023

汉口学院计算机科学与技术学院,湖北 武汉 430212

土壤重金属 自适应进化模型 灰狼算法 Elman神经网络 数据预测

国家自然科学基金湖北省科技重大专项湖北省自然科学基金湖北省自然科学基金湖北省自然科学基金

612722782018ABA0992015CFA0612018CFB4082020CFB761

2024

10.13331/j.cnki.jhau.2024.02.018

湖南农业大学学报(自然科学版)
湖南农业大学

湖南农业大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.868
ISSN：1007-1032
年,卷(期)：2024.50(2)
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