基于混合策略改进的LightGBM土壤污染预测模型

Improved LightGBM soil pollution prediction model based on mixed strategy

社会经济快速发展,然而土壤污染中重金属污染所占的比例越来越大,对生态环境和人们的生命健康造成了巨大的威胁。针对以上问题,提出一种基于混合策略改进的土壤重金属污染预测模型,即先采用随机森林选出最优特征子集,再利用随机搜索对LightGBM参数进行优化,最后通过训练得到的LightGBM模型预测土壤的内梅罗综合污染指数,从而得出土壤重金属污染状况。以我国华北平原的某区域为研究区,并与RS-LightGBM、LightGBM、SVR模型的预测结果进行对比。结果表明,所提模型的均方误差、平均绝对误差相比于LightGBM模型分别降低了69.09%、39.09%,决定系数相比于LightGBM模型提高了6.11%。上述结果表明本论文提出的模型可以有效应用于土壤重金属污染预测研究中。

With the rapid development of social economy,the proportion of heavy metal pollution in soil pollution is increasing,posing a huge threat to the ecological environment and people′s life and health.Aiming at the above problems,this paper proposes a soil heavy metal pollution prediction model based on the improved mixing strategy,that is,the optimal subset of characteristics is selected by random forest,and then the LightGBM parameters are optimized by random search,and finally the Nemero comprehensive pollution index of the soil is predicted by the trained LightGBM model,so as to obtain the soil heavy metal pollution status.A certain area of the North China Plain in China is used as the research area,and the prediction results of RS-LightGBM,LightGBM and SVR models are compared.The results show that the mean squared error and mean absolute error of the proposed model are reduced by 69.09%and 39.09%respectively compared with the LightGBM model.The coefficient of determination is 6.11%higher than the LightGBM model.The above results show that the proposed model can be effectively applied to the prediction of soil heavy metal pollution.