## 3 Results and Discussions
### Prediction of Oil Spill Transport Behavior Based on Machine Learning  
In the case of a sudden river oil spill, in order to intercept and recover the oil spill quickly and efficiently, it is necessary to accurately predict the location of the oil spill and  
Figure 11: **v**, **a**, and **l** for different leakage parameters. Figure 12: Transportation state of an oil spill reaching the 2\(\#\) interception point under two leakage modes. the extent of pollution. Traditional numerical simulation calculations are complex and time-consuming, which undoubtedly prolongs the emergency response time. Given the advantages of machine learning, such as fast computation speed, good model stability, and low dependence on physical mechanisms, this paper adopts the machine learning method to establish the oil spill transport state prediction model to guide the decision-making support of oil spill accidents. In this paper, based on the value range of each variable in Section 3.1.1, a total of 320 sets of numerical tests were conducted, and eight times node data were recorded for each set of tests, which resulted in a total of 2560 sets of oil spill transport state data, and an oil spill transport state data set was established. Among them, the characteristic variables include a total of six dimensions: flow rate, wind speed, wind direction, leakage mode, leakage amount, and time; and the labels include the oil spill migration distance \(\mathbf{D}\), oil spill area \(\mathbf{A}\), and oil spill contamination zone length \(\mathbf{L}\), as shown in Table 1.  
Because of the small amount of sample data, training with deep learning models is prone to lead to the problem of overfitting, which will instead impair the final model results [43]. Therefore, in this paper, traditional machine algorithms are used for predictive modeling. In this paper, using a posteriori thinking to select the optimal combination of algorithms, the dataset is divided into training and testing sets in the ratio of 8:2, and four typical learners, namely Random Forest (RF), Back Propagation Neural Network (BPNN), Extreme Gradient Boosting (XG Boost), and Support Vector Regression (SVR), are used for modeling and analysis [44; 45; 46; 47], and the coefficient of determination (\(R^{2}\)) and 1-Mean Absolute Percentage Error (\(I\)-_MAPE_) are used as the model evaluation metrics to evaluate the prediction performance of the model. Table 2 shows the computation process of the four learners. The modeling scheme is as follows:  
Step 1: Four learners, RF, BPNN, XG Boost, and SVR, are used for modeling respectively, and the Random Search algorithm is applied to select the optimal hyper-parameters, analyze the prediction performance of different learners, and achieve the preliminary selection of learners. Step 2: To further improve the prediction performance of the learner, an intelligent optimization algorithm is used instead of the traditional Random Search algorithm for the optimization of hyperparameters. The prediction performance of different combinations of machine learning algorithms is analyzed, and the optimal algorithm is selected to achieve the prediction model. The predictions of the four training models are shown in Figure 13. For the three labels \(\mathbf{D}\), \(\mathbf{A}\), and \(\mathbf{L}\), the average \(R^{2}\) of the four training models are 0.677, 0.873, 0.717, 0.880; the average \(I\)-_MAPE_ is 0.713, 0.860, 0.707, 0.863. The prediction performance of the BPNN and SVR is significantly better than that of the RF and the XG Boost, and so the BPNN and SVR are initially chosen as the benchmark. Therefore, BPNN and SVR are initially selected as the benchmark models.