Machine-learning models for on-site estimation of background concentrations of arsenic in soils using soil formation factors
Machine-learning models for on-site estimation of background concentrations of arsenic in soils using soil formation factors
复制标题
利用土壤形成因子现场估算土壤中砷背景浓度的机器学习模型
DOI：
10.1007/s11368-016-1374-9
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发表时间：
2016-02
影响因子：
3.6
通讯作者：
Jiao Li
中科院分区：
文献类型：
--
作者：
Jin Wu;Yanguo Teng;Haiyang Chen;Jiao Li
目的考虑土壤环境的空间异质性是进行区域尺度土壤污染评价的基础。虽然有许多方法可以区分自然和人为元素含量，但很少有研究关注现场测定方法，很少有特定地点和敏感的参考资料。在这项研究中，网站的背景浓度估计为土壤污染assessment.Materials和methodsHere的现场参考，支持向量机（SVM）是用来预测网站的背景浓度的基础上，土壤形成的9个影响因素。三个机器学习算法，这被认为是有效的解决优化问题，被用来选择最佳参数的支持向量机。这三种算法分别是：（1）网格搜索算法，（2）遗传算法，（3）粒子群优化算法。结果和讨论模型性能进行了评估，使用平方相关系数和均方根误差。它们随后在土壤污染评估中的应用表明，在环境现场评估中不加区别地使用所有土壤类型的一致参考可能导致低估和高估。这些问题很可能得到解决，通过使用网站的背景浓度预测，建立污染与未污染regions.ConclusionsWe得出结论，支持向量机的基础上土壤形成的影响因素是一种有效的方法，为网站的背景浓度预测，大大提高了背景参考在我们的研究站点的适用性。稍加修改就可以使这种方法适用于其他地区和土壤类型。
PurposeTaking into account great spatial heterogeneity in soil environments is essential to carrying out an accurate soil contamination assessment at the regional scale. Although there are numerous methods for distinguishing between natural and anthropogenic element contents, few studies focus on on-site determination methods, with few site-specific and sensitive references available. In this study, site background concentration is estimated as an on-site reference for soil contamination assessment.Materials and methodsHere, a support vector machine (SVM) is used to predict the site background concentration based on nine influential factors of soil formation. Three machine-learning algorithms, which are considered efficient in solving optimization problems, are used to select the optimal parameters of the SVM. These three algorithms are as follows: (1) a grid search algorithm, (2) a genetic algorithm, and (3) a particle swarm optimization algorithm.Results and discussionModel performances were evaluated using squared correlation coefficients and root-mean-square error. Their subsequent application to soil contamination assessment demonstrated that indiscriminate use of a consistent reference across all soil types in an environmental site assessment may result in under- and over-estimation. These problems are likely to be resolved by using site background concentration predictions to establish contaminated versus un-contaminated regions.ConclusionsWe conclude that a SVM based on factors of influence for soil formation is an effective method for site background concentration prediction and substantially improved the suitability of background references at our study site. Slight modifications would make this approach applicable to other regions and soil types.
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DOI：
10.2136/sssaj1959.03615995002300020021x
发表时间：
1959-03
影响因子：
2.9
作者：
R. Simonson
通讯作者：
R. Simonson
影响因子：
11.8
作者：
Yanguo Teng;Jin Wu;Sijin Lu;Yeyao Wang;X. Jiao;Liuting Song
通讯作者：
Yanguo Teng;Jin Wu;Sijin Lu;Yeyao Wang;X. Jiao;Liuting Song
影响因子：
3.7
作者：
M. Timmerman
通讯作者：
M. Timmerman
影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.
通讯作者：
Williams, Lynne J.
DOI：
10.1016/0160-4120(96)90358-x
发表时间：
1992
期刊：
--
影响因子：
--
作者：
A. Porteous
通讯作者：
A. Porteous