摘要
机油污染物对于农作物生长和土壤基质产生不可忽视的影响,造成农作物减产甚至绝收等现象。为解决土壤表层中机油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3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3种机油的浓度预测结果利用相关系数R P和均方根偏差(root mean square deviation,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%;在实验制备的3种机油样本中,从中各选取浓度为0.01~0.3 mL/g的30组样本进行模型预测验证,对其识别准确率δ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。研究成果为土壤表层中其他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。
Motor oil pollutants have a non-negligible impact on crop growth and soil matrix,causing phenomena such as crop yield reduction and even crop failure.In order to solve the problem of predicting the concentration of motor oil pollutants in the soil surface layer,the fluorescence induction technique was used to obtain the spectral curves of motor oil,and the method of predicting the concentration of pollutant oils in the soil surface layer using the wavelet kurtosis as a quantitative parameter was proposed,and a comparative analysis was carried out by combining the three different kinds of motor oils on the market with the random forest regression algorithm.The experimental results show that the concentration prediction results of random forest with the selected wavelet kurtosis parameter for the three kinds of motor oils are evaluated using the correlation coefficient R P and the root mean square deviation(RMSD),and the prediction of gear oil,engine oil,and motorcycle oil are improved by 1.2%,2.2%,and 1.9%,and 14.9%,32.4%,and 16.8%,respectively.Among the experimentally prepared samples of three kinds of engine oils,from which 30 groups of samples with concentrations of 0.01~0.3 mL/g each are selected for model prediction validation,the recognition accuracy of which is improved by 6.67%,6.66%,9.96%,respectively.It is also verified that the prediction accuracy of the wavelet kurtosis parameter is improved in several regression models with high prediction performance.The research results provide a certain reference for the regression model for the prediction of the concentration of other hydrocarbon pollutants in the soil surface layer,and provides an effective detection means for the sustainable development of agricultural production and soil environment.
作者
姜宁超
景敏
司冰琦
贺兆南
韩亨通
陈曼龙
JIANG Ning-chao;JING Min;SI Bing-qi;HE Zhao-nan;HAN Heng-tong;CHEN Man-long(School of Mechanical Engineering,Shaanxi University of Technology,Hanzhong 723000,China;Shaanxi Provincial Key Laboratory of Industrial Automation,Hanzhong 723000,China)
出处
《科学技术与工程》
北大核心
2024年第12期4843-4850,共8页
Science Technology and Engineering
基金
陕西省重点产业创新链项目(2021ZDLSF06-07)
陕西省自然科学基础研究项目(2022JM-383)
陕西理工大学人才启动项目(SLGRCQD2103)。
关键词
小波峭度
浓度预测
土壤
回归算法
wavelet kurtosis
concentration prediction
soil
regression algorithm
