## 2 Background  
In recent years, the protection of the geological environment has been enhanced. As a result, different approaches to predicting, detecting, and assessing oil spills have been proposed to reduce their consequences [28; 29; 30]. Depending on the types of prediction, detection, and assessment problems being solved and the answers required, particular classification [11] or regression [31] algorithms are used. Both classes of algorithms (classification and regression)are used to predict outcomes in Supervised ML (SML), and both use labelled datasets to train algorithms [32]. However, they are each applied for different SML problems. Classification is a model-finding process used to portion data into discrete classes according to certain features [33]. In classification, the algorithm is trained using the training dataset (input (\(x\))) to categorise data into different discrete classes (output (\(y\))), such as dog or cat, yes or no, healthy or unhealthy, spam or not spam, etc. The most popular classification algorithms are as follows: Linear classifiers [34], Support Vector Machines (SVM) [35], the k-Nearest Neighbours algorithm [36], Decision Tree learning [37], and Neural Networks [11; 12; 20]. Regression is a statistics-based evaluating process used for finding relationships between dependent and independent variables [38]. In regression, the algorithm is trained using the training dataset (input (\(x\))) for the prediction of continuous variables (output (\(y\))) which fall in the range of some allowable intervals, such as predicting market trends, prices, weather forecasting, etc. The most popular regression algorithms are as follows: Gaussian Process Regression (GPR) [39], Support Vector Regression (SVR) [40], Linear and Logistical Regressions [27; 30; 41], and Neural Networks [11; 12; 20]. As we can see, with minimal modifications some algorithms are used for solving both regression and classification tasks, such as Decision Trees and Neural Networks (also see Table 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Domain of Interest** & **Solution for Small** & **Classification/ Regression** & **Methods/Techniques Used** & **Tools Used** & **Dataset for Experiment** \\ \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline
**(4)** & Prediction of heart disease & Surrogate data generation from the characteristics of original observations & & Neural network (NN), Logistic Regression, Random Forest & \begin{tabular}{c} **H** Heart disease data from the UCI Repository [42], \\ **in R** \\ \end{tabular}  \\ \hline
**(10)** &
\begin{tabular}{c} **H** Cardiac \\ **Aditive** \\ **  
One of the main differences between classification and regression is assessing the quality of the prediction [54]. For evaluating classification results, precision and recall performance metrics based on relevance [43; 55] and receiver operating characteristic (ROC) curves are used. For evaluating regression results, statistical tests are used. Predicting the consequences of oil spills on ground environments belongs to the regression group, since we would like to obtain continuous values, such as the weight of the adsorbed oil, the penetrating oil volume, etc. Consequently, in this research, we use the following statistical tests to evaluate the results of the prediction: the Root Mean Square Error (RMSE), the Mean Squared Error (MSE), and the Coefficient of Determination (\(R^{2}\)) [35]. RMSE shows how far the observation values (\(f_{train}\)) are from the predicted data points (\(f_{i}\)) [56] (see Equation (1)). \[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{n}{(f_{i}-f_{train})^{2}}}{n}} \tag{1}\]