# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 1 Introduction  
Maritime accidents are traditionally analysed based on historical technical studies that allow us to identify risk situations. To obtain this identification of risks, a new and more accurate mathematical model will be of interest to improve the prevention of the occurrence of accidents by considering the accident theory [1]. Nowadays, based on these models, and as a consequence of the consideration of technical and human factor points of view, there is a decreased number of accidents [2], although, as per some authors [3], due to the increase in the size of ships, one single incident can have catastrophic and long-term consequences. What is more, the main intention of the International Association of Marine Aids to Navigation and Lighthouse Authorities IALA standards is to give recommendations [4], derived from probability studies [5, 6], that will be available to different countries to control the risk in maritime transport. Despite this, these models are limited to certain working conditions and need to improve the understanding of the human factor, which claims to be the cause of 75 to 96% of maritime accidents [7, 8]. For that reason, authors such as [PERSON] [9] studied the incorporation of the human factor into an accident investigation program, using a Human Factor Analysis and Classification System (HFACS) in order to provide the categorisation of the causal factors in one of nineteen sections, named the coding process. In consequence, there is not a single model that lets us predict all the risks based on the commonly considered variables being developed in the last decades of several research works for collisions [10]. Regarding the influence of the human factor on vessel collisions, [PERSON] et al. [11] carried out research about theinfluence of circadian alterations caused by the action of sunlight on bridge watchkeepers. Another type of research work was centred on certain navigation areas [12], highlighting the need for models when there are incomplete accident databases. In this sense, more recent works have shown statistical studies for the Strait of Istanbul [13]. Based on the previous results, it is interesting to highlight that the human factor is being investigated as the most relevant variable at the time to prevent accidents in fishing vessels [14; 15; 16; 17; 18]. Despite this, it is a complex point of view that is difficult to understand from the variables typically considered by CIAIM reports [19]. These reports are from the Spanish National Official Commission to investigate maritime accidents in the Spanish Search and Rescue (SAR [17]) region. All the reports are on its website and the information from 300 accidents were manually organised for a posterior mathematical analysis. Another group, [PERSON] et al. [20], followed this methodology of investigating marine accidents in the database of official governments--in that case, the UK Marine Accident Investigation Branch (MAIB). In that work, the HFACS method was used, and multiple linear regression was obtained in order to determine the relationship between the number of accidents and the most common causal factors. Although it is widely extended, even as a myth, according to some authors [21], that more than 80% of maritime accidents are related to the human factor, a few studies have proposed novel models using actual AI tools, pointing out the specific weight and influence of all the factors in the shipping industry. In this sense, a new approach was used in previous works in order to define an initial qualitative causal model, and the Bayesian Belief Network (BBN) model was used for the Spanish Search and Rescue (SAR [22]) region. This technique was previously used in another aviation sector for modelling accident investigation reports for aviation safety assessment [23]. The results showed that commonly used variables like visibility and sea conditions were the worst (least probable factors) to define accidents for this region, and statistical models were obtained.