Our team is quite passionate about sustainability as it is a very important issue in the world. Therefore, we chose this project to educate more people about the importance of sustaining our planet through reducing climate change, air pollution and conserving biodiversity. To tackle this challenge, we decided that we need to promote it to people of all ages. Different generations nowadays are exposed to different things, so we realised that we should approach them through different means. That is the reason why we settled on using 3 applications to target different audiences. 

We developed the Discord bot using discord.py, and wrote the code in python, on repl.it. The website was created by Weebly and the app was made using an app inventor. Tableau was used for visualization of data in graph form. The Seasonal naive and ARIMA model were used for writing our machine learning model.

We have faced several problems but also accomplished some milestones while creating our project. While developing the bot, we ran into many bugs, and a lot of work was put into making the leaderboard work smoothly. When the Hackathon bot finally added us into the leaderboard, we were so excited! The app has not been a smooth ride either. Since one of our team members has made a similar app before, we tried to merge that project into our current one, and it was harder than we imagined. However, the satisfaction of being able to play our game afterwards made it worth it. As for the website, we came across different problems including difficulty in formatting, making a favicon, and especially finding and using data for machine learning. None of us are very experienced in machine learning, so it was a challenge. We certainly learned a lot through this, and we accomplished in producing a reasonable prediction and displaying all our research on the website.

The space agency data is used in two ways.

First, we analyzed global temperature data and sea level data collected by NASA and created visualizations to present them in a straightforward manner. The temperature of the Southern Hemisphere and the Northern Hemisphere are also analyzed. The application Tableau was used to create area charts, scatter plots and line charts. The rising sea level trend is compared to the rate of the melting ice sheets in the North and South pole. The graph can alarm people and convey a message that it will be too late if we do not take action now.

Secondly, we wrote a machine learning model using the Seasonal naive and ARIMA model to analyze the global temperature data from 1980 to 2015 and predict the future temperature trend. We also implemented visualizations by importing the “ggplot2” package into R Studio, so new insights to the problem can be gained. During these 4 years, global warming has intensified rapidly. As the csv table doesn’t have data in 2016-2020, we can see that the temperature is predicted to be kept at a steady level. The global temperature in previous years has been much lower, so the prediction result shows that in order to keep our temperature constant and reduce climate change, we have to take care of our planet.

Link to video in Google Drive: https://drive.google.com/file/d/1W9mRcCJ0rbscQXCYztmudwOG39AlXaWT/view?usp=sharing

Bot:

• All references used for the bot are credited in the bot interactions.
• https://www.conservation.org/stories/11-climate-change-facts-you-need-to-know
• https://www.who.int/features/factfiles/climate_change/facts/en/index2.html
• https://davidsuzuki.org/what-you-can-do/top-10-ways-can-stop-climate-change/
• https://thesmartcave.com/led-vs-cfl/
• https://en.wikipedia.org/wiki/Greenhouse_gas
• https://edition.cnn.com/2019/06/03/world/gallery/surprising-sources-greenhouse-gas-emissions-intl/index.html
• https://climate.nasa.gov/
• https://www.jpl.nasa.gov/edu/teach/activity/graphing-sea-level-trends/
• https://www.jpl.nasa.gov/edu/news/2019/10/18/nasas-eyes-on-extreme-weather/
• https://earthobservatory.nasa.gov/blogs/climateqa/what-can-we-do-about-global-warming-2/

Website:

• Images:
• https://pixabay.com/
• https://en.wikipedia.org/wiki/Coral_bleaching
• https://en.wikipedia.org/wiki/R_%28programming_language%29
• Climate Change:
• https://www.mfe.govt.nz/climate-change/why-climate-change-matters/evidence-climate-change
• https://climate.nasa.gov/evidence/
• https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
• https://youmatter.world/en/definition/sea-level-rise-definition/
• https://www.canada.ca/en/environment-climate-change/services/climate-change/causes.html
• https://www.noaa.gov/education/resource-collections/climate/climate-change-impacts
• https://data.giss.nasa.gov/
• https://www.jpl.nasa.gov/edu/teach/activity/graphing-sea-level-trends/
• Machine learning:
• https://towardsdatascience.com/advanced-time-series-analysis-with-arma-and-arima-a7d9b589ed6d
• Biodiversity:
• https://www.science.org.au/curious/earth-environment/climate-change-and-biodiversity
• https://www.wikihow.com/Help-Protect-Biodiversity
• https://www.globalissues.org/article/170/why-is-biodiversity-important-who-cares
• https://www.greenfacts.org/en/biodiversity/
• Air pollution:
• https://en.wikipedia.org/wiki/Air_pollution
• https://www.activesustainability.com/climate-change/link-between-climate-change-air-pollution/
• https://www.mass.gov/files/documents/2016/08/vl/health-and-env-effects-air-pollutions.pdf
• https://www.wikihow.com/Take-Action-to-Reduce-Air-Pollution
• https://www.epd.gov.hk/epd/english/environmentinhk/air/prob_solutions/strategies_apc.html

App:

• https://thumbs.dreamstime.com/t/underwater-cartoon-flat-background-fish-silhouette-seaweed-coral-ocean-sea-life-cute-design-vector-illustration-134039955.jpg
• https://toppng.com/marlin-finding-nemo-characters-nemo-PNG-free-PNG-Images_180342?search-result=nemo-transparent-background
• https://image.shutterstock.com/image-vector/plastic-bag-fly-sky-pop-260nw-1035293188.jpg