基于小波峭度的土壤表层机油浓度预测方法应用

机油烃类物质对于农作物生长和土壤基质产生不可忽视的影响,造成的农作物减产甚至绝收等现象。为解决土壤表层中机油烃类污染物质浓度预测的问题,利用荧光诱导技术获得机油光谱曲线,提出以小波峭度作为量化参数进行土壤表层中污染油浓度预测的方法,以市面出售三种不同机油结合随机森林回归算法进行了比较分析。实验结果表明,选取小波峭度参数的随机森林对三种机油的浓度预测结果利用相关系数R_p和均方根偏差RMSD进行评价,对齿轮油、发动机油、摩托车机油的预测,分别提高了1.2%、2.2%、1.9%和14.9%、32.4%、16.8%;对三种机油随机选取30组样本,对其识别准确率分别提高了6.67%、6.66%、9.96%;同时也验证了小波峭度参数在多个回归模型中的预测精度均有提高,具有较高的预测性能。综上所述,这为土壤表层中其它烃类污染物浓度预测回归模型提供了一定的参考,为农业生产和土壤环境的可持续发展提供一种有效的检测手段。

Application of wavelet kurtosis based method for predicting soil surface oil concentration

Oil hydrocarbons have a non-negligible impact on crop growth and soil matrix, resulting in crop reduction and even loss of harvest. In order to solve the problem of predicting the concentration of hydrocarbon pollutants in the soil surface, the spectral curve of oil was obtained by fluorescence induction technology, and the wavelet kurtosis was proposed to predict the concentration of polluted oil in the soil surface. Three different oils on the market were compared and analyzed by random forest regression algorithm. The experimental results show that the correlation coefficient R_p and the root mean square deviation RMSD are used to evaluate the prediction results of the three kinds of oil concentrations. The prediction results of gear oil, engine oil and motorcycle oil are increased by 1.2%, 2.2%, 1.9% and 14.9%, 32.4%, 16.8%, respectively. Thirty groups of samples were randomly selected for three kinds of oil, and the recognition accuracy was improved by 6.67%, 6.66% and 9.96%, respectively. At the same time, it is verified that the prediction accuracy of wavelet kurtosis parameter in multiple regression models is improved, and it has high prediction performance. In summary, this study provides a certain reference for the regression model for predicting the concentration of other hydrocarbon pollutants in the soil surface, and provides an effective detection method for agricultural production and the sustainable development of soil environment.