## 7 Conclusions  
The analysis of the related works on the application of fuzzy inference and ML methods when predicting with small datasets showed that a small dataset prediction issue exists in different research areas, and its solution tends to be solved in similar ways: (1) most commonly, using the surrogate data generation approach; (2) applying unsupervised learning; (3) using an interpolation approach for data augmentation; (4) selecting useful predictive features of data samples; (5) collecting data from users; and (6) using convolutional networks that naturally operate on an input of any size. The analysis of these approaches and the environment for the prediction of the consequences of oil spills on the ground environment showed that the most suitable approach is generating a surrogate dataset, since the process of an oil spill can be specified using a mathematical model. Moreover, applying fuzzy logic allows us to flexibly describe the oil spill process.  
The proposed approach for the prediction of the consequences of oil spills using small sets of data allows us to investigate the suitability of ML algorithms (Linear Regression, Decision Trees, SVR, Ensembles, and GPR) and fuzzy inference methods to predict the consequences of oil spills using small sets of data. A small dataset was enlarged with the synthetic data generated by the mathematical model, and the same generated dataset was used for experimentation with fuzzy inference and ML methods for prediction. The main advantage and contribution of the proposed approach is that it allows us to perform a statistical comparison of the application of the fuzzy inference and ML methods for predicting the consequences of oil spills using small sets of data.  
The advantage and contribution of the current research is the proposed conceptual architecture for predicting the consequences of oil spills using small datasets, which could be adapted for users' needs by adding new prediction methods and techniques and using different implementation tools.  
The obtained results showed that the proposed ANFIS-based approach (MSE = 0.12; \(R^{2}\) = 0.95; NRMSE = 4.3%) has the best prediction performance and performance results. Consequently, in the case of small datasets, it is suitable and can be successfully applied for predicting the consequences of oil spills.  
In future works, we will increase the number of prediction experiments with various oil spill datasets. We plan to expand and use our proposed approach to analyse and predict oil product migration with groundwater. During these experiments, we have produced a large set of fuzzy rules. A standard personal computer does not have enough computing resources for high-quality and large-scale training and predicting oil spill consequences. Therefore, in the future, we plan to develop a method for transferring the proposed ANFIS-based approach to a cloud server. Another important direction is developing an approach for tuning and optimising fuzzy rules.  
Conceptualization, D.K.; methodology, A.B. and D.K.; software, A.B.; validation, A.B. and D.K.; formal analysis, A.B. and D.K.; investigation, A.B. and D.K.; resources, A.B.; data curation, A.B.; writing--original draft preparation, A.B. and D.K.; writing--review and editing, A.B. and D.K.; visualization, A.B. and D.K.; supervision, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.