## 1 Introduction  
In the real-world application of prediction, we often face the challenge of small-labelled data [1] or small sets of data [2], such as when detecting outliers [3], detecting new trending topics, diagnosing a rare disease [4, 5], predict emerging social or natural events [6], predicting river flooding [7], etc. However, to provide sufficiently accurate predictions even with uncertainty, data-based prediction approaches, like machine-learning (ML) techniques and fuzzy inference systems (FIS), need large datasets. Predicting the consequences of an oil spill on the ground environment also falls within the realm of prediction with small datasets [8, 9], since collecting real data from the extreme situations of oil spills is very time consuming and expensive. We can find different approaches to solving the small dataset issue in the literature. The authors of [4] generated a surrogate dataset for heart disease prediction based on original case studies. The authors of [10] showed that an appropriate choice of regression algorithm and input parameters allows us to build accurate surrogates for simulations in additive manufacturing. The authors of [11] used a cluster analysis approach to generate surrogate data in the medical domain. In [12], the authors applied a deep neural network (DNN) to integrate similarity features and perform learning to predict software defects. As can be seen from these works, synthetic data generation from the existing small datasets of real oil spills could be used in designing experiments, and these datasets are relatively inexpensive to develop and use [10]. However, for efficiency, the generation of synthetic oil spill data must be accurate enough for the task of prediction. Moreover, the process of an oil spill and its penetration into the ground environment is complex and vague due to its nature and the dynamic environment (i.e., temperature, sun, and wind) [13; 14; 15]. All of these factors lead to inaccurate initial qualitative data [16; 17] and subjective expert opinions regarding oil spill consequences that are difficult to understand [18; 19]. Consequently, we need an effective and intelligent prediction model to facilitate the selection of suitable response methods [5]. This motivates some researchers to apply the adaptive neural fuzzy inference system (ANFIS) for oil spill prediction. According to [20; 21], the application of this method in modelling and the prediction of various complex processes allows us to achieve more successful and accurate results. ANFIS is used in this study because it combines the advantages of artificial neural networks (ANNs) (i.e., self-learning from available data) and FIS (i.e., human-like reasoning) [22; 23]. However, despite existing research and approaches, prediction is challenging when we have only a few real entities of oil spills, and the oil spill process remains fuzzy. Therefore, it is unclear which prediction approach is best for predicting the consequences of oil spills on ground environments when only a small dataset is present. Consequently, in this paper, we aim to assess the appropriateness of several ML techniques and ANFIS for predicting the consequences of oil spills on ground environments when only small datasets are present.