A few weeks ago, I had the opportunity to participate in a hackathon at MIT, and for my first ever hackathon, I must say I thoroughly enjoyed it. I got to meet some super interesting and smart people who were all enthusiastic about using AI to improve the world. From meeting a computer scientist in Brazil to a professor of Ethics, I was immersed in diversity.
Within the five focus areas of the hackathon(Poverty, Education, Justice, Climate Change and Health) I chose Climate Change because I believe there is huge potential in using AI to help solve the issue of climate change. I joined a group of 4 other people who were equally passionate about climate change, and we named ourselves the Climate Hackers.
The majority of the first day was spent finding and organizing data sets. First, we had to find the data set. We were given two data sets which consisted of monthly climate data in Brazil and dates on which there were forest fires. The two data sets did not match up, one had a monthly frequency, while the other had a daily frequency. Mentors at the hackathon suggested to find data with daily, and maybe even hourly frequency. This meant that we had to find two data sets with hourly frequency. After several hours, I found a data set with daily accuracy for climate in Brazil with 18 different parameters and a data set provided by NASA which gave hourly frequency of the forest fires.
Now came data organization. Being a novice at coding made data organization a bit more challenging. However, with the help of some of the more experienced coders on my team I was able to organize the NASA data set. Some of our other group members organized and visualized the climate and NASA data set, and prepared them for the neural net.
After a day of data, then came the fun part; the neural net. Unfortunately, I was unable to participate in the development of it but I do know we built a Light GBM. A light GBM is a gradient boosting algorithm which is based on a decision tree, allowing for a ranking of variables to be created. This is especially helpful for our situation because if we are able to visualize the importance of climate data (max temp, altitude, etc.) it can be used to help identify the signs of a potential forest fire…