# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Neural Networks  
Several curve-fitting processes were employed in previous works, like, for instance, minimum square error curve fitting and, in a posterior multivariable curve fitting, response surface modelling. Despite this, these models were obtained with a decreased determination factor that will not let us obtain a model for posterior predictive applications in maritime accidents. In recent years, artificial intelligence procedures have shown a great increase in the accuracy of their proposed models. In particular, neural networks show clear advantages. In this sense, a neural network can be defined as a complex system comprising nodes and links represented by neurons and their connections [27]. The use of this network can be employed for curve fitting.  
In this study, feedforward neural networks with 10 layers were employed, and 70% of the data was employed in the training and validation process and the 30% remaining data in the test analysis. These networks were employed to model ship accidents but with special care in the number of layers, always employing a minimum number, to prevent overfitting and, in consequence, a decreased predictive capacity of the model.  
Figure 4: Number of clusters of the proposed data.  
Figure 3: Importance of each variable to deduce the type of accident.  
Figure 2: Importance of each variable to deduce the type of accident.  
Figure 5: Relation between clusters and the number of accidents.  
Figure 6: Representation of the cluster number versus the type of accidents and compliance with the minimum number of crew members.  
Figure 8: Representation of the cluster number versus the type of accidents and beam dimensions.  
Figure 7: Representation of the cluster number versus the type of accidents and ship type.  
Figure 10: Error histogram of the trained neural network.  
Figure 9: Representation of the cluster number versus the type of accidents and year of construction.  
From these figures, it can be concluded that the most common types of ships are 2 and 3, which are passenger and/or cargo vessels and fishing vessels for both of the ships implicated in the accident. The most common types of accidents are types 2 and 10, which are flooding and operational accidents. The construction year is near the year 2000, the wind force was shown as a gentle breeze when the accident happened and sea conditions were smooth. The swells were homogeneous in a number of cases and values, and in most of the cases, visibility was good. The moment of the accident (day or night) was homogenous for all of the 276 accidents analysed. Finally, ship length was the minimum for both ships, so it was associated with the fishing vessels. If both ships complied with the minimum number of crew members was analysed at the time (values 1 (one ship) and 5 (two ships)). In consequence, the most common accidents were related to fishing vessels, passenger ships and due to flooding or operational accidents in the year 2000 under good weather conditions (which reinforces the conclusion that the accidents were due to operational actions and, in consequence, human factor consequences).