## 3 Related Works  
This section reviews approaches, methods, and techniques used in ML to solve the issues caused by small datasets in different application areas. They are summarized in Table 1.  
Table 1 summarizes the relevant related works on prediction with small datasets in various application domains. Consequently, the table consists of seven columns as follows: (1) reference for the publication; (2) domain of interest, describing the application domain of prediction with small datasets; (3) solution to the issue of small data, presenting approaches used to overcome the small dataset issue; (4) classification/regression, identifying the type of prediction; (5) methods/techniques used in the research; (6) tools used to implement the proposed approach; and (7) dataset for the experiment.  
As can be seen from Table 1 Column 2, the small dataset issue exists in different research areas including medicine [39], studying the chemical and physical properties of various materials [20; 45; 47], energy optimisation [11; 31; 46], marine oil spills [11], etc. Despite different research areas, the small dataset issue tends to be solved in similar ways (Table 1 Column 3). Most authors use the surrogate data generation approach [4; 50], whilst some use unsupervised learning, such as the cluster analysis approach [11] and deep neural networks [11; 12; 20], to increase the amount of data. Other authors suggest the use of an interpolation approach for data augmentation and data visualisation [46; 49], whilst some select useful predictive features of data samples [12; 47]. The authors of [31] propose an approach where users enter data for prediction, whilst the authors of [51] conclude that their proposed approach of two ML models based on a convolutional network can naturally operate on an input of any size.  
Column 4 in Table 1 shows that despite the differences in classification and regression, the same methods/techniques (Table 1 Column 5) were used to make predictions. As a rule, most authors applied ML approaches, such as various kinds of regression algorithms, k-nearest neighbours, SVM, Decision Trees and Ensembles [4; 45; 48]. In the analyzed papers, most authors also applied deep neural networks [4; 12; 20; 50], arguing that this approach is optimal for solving predictions with small datasets. Some authors [11; 44] used unsupervised learning algorithms when indicating dependence in the data features was difficult. Several authors [31; 38; 45] showed optimization methods for predictive algorithms, such as cross-validation, gradient descent, and the use of fuzzy logic.  
Modern tools simplify the implementation of prediction algorithms (Table 1 Column 6). The authors of [4; 47] used various programming languages, such as C++ and R. Most authors argued that the Scikit-Learn library, Tensorflow, and KERAS, from Python, are convenient for data analysis and deep analysis learning methods [12; 20; 49]. The authors of [12] used MATLAB modules for ML, whilst the authors of [31] proposed the implementation of ANFIS from MATLAB to implement predictions with fuzzy logic.  
Column 7 shows the datasets that the authors used for predictions. In most cases, this data concerned real objects or processes [4; 20; 44; 45]. The authors of [10] proposed an approach for generating a dataset based on two previously built models.  
Summing up, in this research, the most suitable approach to solving the small dataset issue for oil spill prediction is generating a surrogate dataset, since the process of an oil spill can be specified using a mathematical model [13]. According to the related works presented in Table 1, it is reasonable to use several ML methods for oil spill prediction and apply fuzzy logic to describe the vagueness of the oil spill process on the ground environment.