Determining Factors Shaping Garbage Generation on Cruise Ships in the Arctic Region Through Advanced Deep Learning Models

Global Maritime Congress, İstanbul, Türkiye, 20 - 21 Mayıs 2024, ss.244-247

The Arctic Region is experiencing a surge in tourism, fuelled

by cruise expeditions organized by tour companies, indicating

a growing tourism sector. Climate change has rapidly altered

the Arctic, reducing sea ice cover and opening new sea routes,

making it more accessible. However, cruise ships, crucial

for Arctic tourism, pose environmental challenges, notably

in garbage generation. Waste disposal is governed by the

MARPOL agreement and the Polar Code, with regulations

on garbage discharge. Storing waste onboard until reaching

designated port facilities presents challenges. This study

utilized PAME-provided data on passenger ship garbage

generation, employing deep learning methods to analyse

factors like gross tonnage, fuel quality, passenger capacity,

distance travelled, and date of build. K-means clustering

categorized garbage values into ‘less’ and ‘more’ clusters,

and models with various feature combinations were created.

The combination of distance travelled, passenger capacity,

and gross tonnage yielded the highest test success (F1 = 0.94)

with the fewest attributes. The study concluded by estimating

garbage generation by cruise ships in the Arctic.