## 1 Introduction  
Deforestation is the loss of tree cover over a given time period, usually as a result of forests being cleared for other land uses, such as farming or ranching [1]. Over the past years, there has been a large amount of deforestation, which has affected the ecosystem. Deforestation is recognized as an issue that has negative effects in the ecosystem. There are many cases where people deforested areas without following rules and regulations [2]. Between 2015 and 2020, the rate of deforestation was estimated to be 10 million hectares per year [3]. Deforestation is caused by human and natural factors. Natural factors include forest fires and forest tree diseases. On the other hand, human activities are among the main causes of global deforestation. In fact, deforestation usually occurs when forests are cleared for human use, such as creating new houses and establishing new plantations. According to the Food and Agriculture Organization (FAO), the expansion of agricultural areas has caused nearly 80% of global deforestation. The remaining causes of deforestation are diverse, including the construction of infrastructures, such as roads or dams, and mining activities and urbanization [4]. Due to the accelerated deforestation in the last decades, and considering the importance of forest and its impact on the global environment, deforestation needs to be analyzed and compensated in order to reduce its negative effects on the ecosystem and to promote sustainable development [5]. One of the aspects of deforestation is the production process of palm oil, which is an edible vegetable oil that comes from the fruit of oil palm trees [6]. Palm oil production can lead to the clearing of new land to create more space for plantations or to establish new plantations. One major issue is that palm oil production is not easy to oversee, as large areas are cultivated by smallholders. Nongovernment institutions, such as the Roundtable on Sustainable Palm Oil (RSPO), are trying to tackle these problems and aim to ensure a sustainable palm oil production [7]. In addition, we have witnessed a growing availability of multisource forest data due to new remote sensing methods and techniques [8]. Several studies have demonstrated the usefulness of remote sensing images as an information source for forest management [9; 10]. Forest satellite images have been processed using machine learning (ML) techniques [11; 12; 13]. Machine learning is a form of artificial intelligence, in which a computer is algorithmically trained to perform a task, such as event prediction or image classification [14; 15]. The advantages of machine learning include flexibility and scalability compared with traditional statistical techniques, which makes it deployable for many tasks, such as phenomena stratification/clustering, classification, and predictions. Another advantage of machine learning algorithms is the ability to analyze diverse data types (e.g., geospatial data and descriptive data) and incorporate them into predictions [16]. Recent works show that machine learning techniques have proved to be an effective solution to detect deforestation [17; 18; 19]. While several works have shown promising results in detecting deforestation related to oil palm, there has been no research study that focuses on the prediction aspect of deforestation related to oil palm (i.e., oil palm plantations and palm oil production). Additionally, with regard to the application of machine learning in preventing deforestation, existing works have not assessed the criteria considered in model training. Consequently, there is still uncertainty about the effect of each criterion in the deforestation prediction. This research study focuses on the prediction aspect of deforestation related to oil palm. It aims to reduce uncertainty about the effect of each criterion by evaluating the influence of each criterion in the prediction of oil palm deforestation. The main contribution of our approach relies on focusing on the prediction of deforestation related to oil palm.