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⁻¹. 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 (R²). 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 R² –0.80.

环境污染物，特别是来自总石油烃（TPH）的污染物，对土壤的化学，生物和物理影响极为复杂。在这里，我们通过对温室条件下萨哈林岛的11种土壤样品的TPH急性植物毒性作用建模来研究这种影响。土壤被原油污染，剂量范围为3.0–100.0 g kg ^-1。测量大麦我们已经估算了根伸长率，这是至关重要的生态毒性参数。我们还研究了不同土壤中的对比效果。为了预测TPH的植物毒性，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）和确定系数（R ²）证明了所讨论的模型是有效的。我们已经表明，ANN和SVR可以基于土壤化学特性（pH，LOI，N，P，K，粘土，TPH）成功预测大麦响应。达到的最佳精度如下：MAE – 8.44，RMSE –11.05和R ² –0.80。