In order to quickly and quantitatively measure the diesel content in diesel-contaminated soil and expand the application of near-infrared spectroscopy technology in monitoring the contaminated soil,a diesel-containing kaolin sample set F1 and a diesel-containing actual soil sample set F2 are prepared,and their near-infrared spectra are detected,and three prediction models including BP neural network,random forest and support vector machine regression are established according to the detection data.The results show that the prediction models have good prediction accuracy for sample set F1,but cannot accurately predict the diesel content in sample set F2.Then,the prediction of diesel content in actual soil is improved through spiking and repeated spiking.The near-infrared spectral model established from diesel kaolin samples is predicted after adding a small amount of actual soil samples.Compared with direct modeling,it can predict diesel content more accurately.
关键词
近红外光谱/柴油污染土壤/定量预测模型/加标和重复加标
Key words
near-infrared spectroscopy/diesel contaminated soil/quantitative prediction model/spike and repeated spikes
维普
万方数据