Abstract:
With the advancements in the maritime industry, which delivers almost 90% of the
world trade, the frequency of maritime activities has drastically increased resulting a
major concern in maritime safety. According to the latest accident investigation
publication by European Maritime Safety Agency (2018), there had been a total of
10,384 maritime accidents reported causing 297 deaths, 273 very serious casualties
and 127 ships sunk during the period from 2015 Q1 to 2018 Q2 within the European
Union only. A recent research by Zhang and Li (2017) has discovered that a significant
30% of maritime accidents are caused due to bad weather conditions like sea storms
which was further researched by Goerlandt et al. (2016) and Bitner-Gregerse et al.
(2016) and strong winds caused due to high turbulence and high waves as discussed
by Maritime Injury Center (2019). These deaths and casualties would have been
minimized if there was a mechanism for efficient emergency response as discussed by
Wróbel et al. (2017).
ASVs have been used for several disaster mitigation and recovery operations in
hurricanes such as Wilma (2005), Ike (2008) and the Tōhoku earthquake and tsunami
(2011) according to Xiao et al. (2017). Therefore, ASVs could be used for emergency
response since they are comparatively cheap and safe to be deployed on to hazardous
zones in the deep sea since they have long term marine presence because they are
mostly powered by wave energy, solar energy and wind energy as discussed by Meinig
et al. (2015) and Zhou et al. (2015). A more efficient way for emergency response by
the ASV would be, the ability to predict a location where there is a possibility for an
accident to take place and position itself such that it could effectively respond to the
emergency. Hence the author is proposing an optimal solution using machine learning
techniques to suggest the waypoints to ASVs for effective emergency response on
human operated surface vessels
