Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026, Moscow, Russia.
Faculty of Soil Science,Lomonosov Moscow State University, 119991, Moscow, Russia.
Ecotoxicol Environ Saf. 2020 May;194:110410. doi: 10.1016/j.ecoenv.2020.110410. Epub 2020 Mar 9.
Environment pollutants, especially those from total petroleum hydrocarbons (TPH), have a highly complex chemical, biological and physical impact on soils. Here we study this influence via modelling the TPH acute phytotoxicity effects on eleven samples of soils from Sakhalin island in greenhouse conditions. The soils were contaminated with crude oil in different doses ranging from the 3.0-100.0 g kg. Measuring the Hordeum vulgare root elongation, the crucial ecotoxicity parameter, we have estimated. We have also investigated the contrast effect in different soils. To predict TPH phytotoxicity different machine learning models were used, namely artificial neural network (ANN) and support vector machine (SVM). The models under discussion were proved to be valid using the mean absolute error method (MAE), the root mean square error method (RMSE), and the coefficient of determination (R). We have shown that ANN and SVR can successfully predict barley response based on soil chemical properties (pH, LOI, N, P, K, clay, TPH). The best achieved accuracy was as following: MAE - 8.44, RMSE -11.05, and R -0.80.
环境污染物，特别是总石油烃（TPH），对土壤具有高度复杂的化学、生物和物理影响。在这里，我们通过在温室条件下对来自萨哈林岛的 11 个土壤样本进行 TPH 急性植物毒性效应建模来研究这种影响。土壤被不同剂量的原油污染，范围从 3.0-100.0 g kg。测量关键的生态毒性参数大麦根伸长，我们进行了估计。我们还研究了不同土壤中的对比效应。为了预测 TPH 植物毒性，使用了不同的机器学习模型，即人工神经网络（ANN）和支持向量机（SVM）。使用平均绝对误差法（MAE）、均方根误差法（RMSE）和决定系数（R）证明了所讨论的模型是有效的。我们已经表明，ANN 和 SVR 可以基于土壤化学性质（pH、LOI、N、P、K、粘土、TPH）成功预测大麦的反应。最佳的准确性如下：MAE-8.44、RMSE-11.05 和 R-0.80。
