Prediction of manually controlled vessels’ position and course navigating in narrow waterways using Artificial Neural Networks

Abstract

Despite modern navigation devices, there are still some problems for navigating of vessels in narrow waterways because of geographical structures and various disturbances. In this study, a guidance and an early warning method by means of predicting three-minute-ahead position of a vessel, especially in the turning points, has been developed for navigating in narrow waterways. The Istanbul Strait has been specifically studied as a model. Since operators in Vessel Traffic Services (VTS) can watch only straight bearing of vessels on VTS panels but especially for turning regions, they have to foresee a risk on time which may result in a disaster. The objective of this study is to predict the future coordinates of a manually controlled vessel using Artificial Neural Networks (ANN). Artificial Neural Networks have been trained by using position and speed data collected from vessels which navigated manually in the Strait. Three-minute-ahead position of vessels has been predicted by using the trained ANN. Some on-line experiments have been done in Istanbul VTS centre and it has been observed that the method satisfied the goal in especially turning points of the Strait. Hence the proposed method could be utilized for warning system by VTS operators and guidance system by vessel crew.