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范俊楠,张钰,贺小敏,郭丽,施敏芳,陈浩.基于BP神经网络的重点行业企业周边土壤重金属污染预测及评价[J].华中农业大学学报,2019,38(4):
基于BP神经网络的重点行业企业周边土壤重金属污染预测及评价
BP neural network based prediction and evaluation of heavy metal pollution in soil around the enterprises in key areasof Hubei Province
投稿时间:2018-12-20  
DOI:
中文关键词:  土壤; 重金属污染; BP神经网络; 内梅罗污染指数; 人工神经网络  污染预测  土壤评价
英文关键词:soil  heavy metals pollution  BP neural network  Nemerow pollution index  artificial neural nets  pollution prediction  soil evaluation
基金项目:国家环保公益性科研项目(201509031);2017年土壤污染防治专项湖北省土壤污染风险评估与成因研究项目
作者单位E-mail
范俊楠 湖北省环境监测中心站武汉 430072 112115590@qq.com 
张钰 湖北省计量测试技术研究院武汉 430223  
贺小敏 湖北省环境监测中心站武汉 430072 39208454@qq.com 
郭丽 湖北省环境监测中心站武汉 430072  
施敏芳 湖北省环境监测中心站武汉 430072  
陈浩 华中农业大学理学院武汉 430070  
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中文摘要:
      对湖北省重点区域行业企业周边土壤理化指标和重金属含量进行监测;利用监测数据建立含有13输入、1个隐含层和6输出的3层BP神经网络模型,预测监测区域Mn、Co、V、Ag、Tl、Sb含量,综合重金属监测结果和预测结果,采用内梅罗指数对研究区域进行污染评价。结果表明,研究区域重金属存在不同程度超标情况,最大超标倍数范围为1.8~156.1倍;Mn、Co、V、Ag、Tl、Sb等6项重金属预测结果与实际测试结果相对误差范围在0.3%~19.9%,Mn、V、Ag、Tl、Sb在置信度为99%时均呈显著性相关(P<0.01,n=11),Co在置信度为95%时呈显著性相关(P<0.05,n=11),构建的BP神经网络预测模型具有良好的精准度;基于BP神经网络模型预测结果的内梅罗污染指数未超过警戒限的比例为77.3%,达轻度污染比例17.4%,达中度、重度污染比例均为4.0%。
英文摘要:
      The physical and chemical indicators and heavy metal content in the soil around the enterprises in key areas of Hubei Province were monitored. The monitoring data were used to establish a 3 layer BP neural network model with 13 inputs,1 hidden layer and 6 outputs.The content of Mn,Co,V,Ag,Tl,Sb in the monitoring area were predicted. The Nemerow index based on the monitoring and prediction results of heavy metals was used to evaluate the pollution of the area studied. The results showed that there were different levels of exceeding standard of heavy metals in the area studied. The maximum over standard range was 1.8 156.1 times. The relative error between the prediction results of six heavy metals including Mn,Co,V,Ag,Tl and Sb and the actually tested results was ranged from 0.3% to 19.9%. Mn,V,Ag,Tl,and Sb were significantly correlated with the confidence of 99% (P<0.01,n=11).Co was significantly correlated with confidence of 95% (P<0.05,n=11). The BP neural network prediction model constructed had good accuracy. Based on the BP neural network model,the Nemerow pollution index not exceeding the warning limit took over a proportion of 77.3%,with the proportion of light pollution of 17.4% and the ratio of moderate to severe pollution of 4.0% each.
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