E02 : Using machine learning to predict the effects of nitrous oxide in relation to hypoxic zones


Students Zacharaya Araya
School HDSB - Forest Trail Public School - Oakville
Level Junior 7/8 - Grade 8
Group Group 11 - Engineering and Computing IV
Abstract The purpose of this project is to use principles of machine learning to predict and analyze the effects of nitrous oxide in relation to ocean dead zones. This project uses an algorithmic predictive formula to find the trend and the average increase of the nitrous oxide emissions relative to carbon dioxide emissions. Using this information, this project attempts to demonstrate the direct correlation between hypoxic zones and a continuous increase in global nitrous oxide emissions. Nitrous oxide (N2O) is a long-lived potent greenhouse gas that has an average atmospheric lifetime of 114 years. It contributes towards ozone depletion and global warming. Ocean dead zones are a net source for nitrous oxide emissions, accounting for about 25% of global N2O emissions.
Awards
Group Award Prize
Canadian Institute of Mining Metallurgy and Petroleum (Hamilton Branch) AwardsCanadian Institute of Mining Metallurgy and Petroleum (Hamilton Branch) Award$ 100
Nelson Steel Awards Nelson Steel Award$ 200
Merit AwardsSilver Merit Award$ 80
Laurentian Chapter of SETAC AwardsLaurentian Chapter of SETAC Award$ 100
Certificate
Talkit.ca Computer Engineering AwardsTalkit.ca Computer Engineering Award - First$ 100
Canadian Meteorological and Oceanographic Society AwardsCanadian Meteorological and Oceanographic Society (CMOS) Award - Second$ 50
one year Free membership to CMOS