High-Level Project Summary
Our project uses satellites to scan through the oceans and process them through a regression machine learning model. This regression model aims to find the relationship between the pollution level of a specific ocean area and then it will suggest the best possible ways to cleanse the area. Our machine learning models will include a database of all the marine pollution incidents of the past so that once it scans through the oceans and the results come out, other than quantifying the pollution level of that specific ocean part, it will also provide the best cleansing way of it. Cleansing methods are of various kinds because marine debris is of many kinds too such as plastics, used tools etc.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
In our project. we are mooting an idea of implementing a machine learning algorithm in processing the data collected by the satellites. The satellites can scan the oceans to know the pollution level of the ocean, by scanning through the oceans, it will know the surface pollution level of that area by detecting the concentration level of that debris and quantify it. Normally, the more concentrated, the more polluted it is, and the more polluted, the higher the number it will display.
And then, after collecting the data of those pollution levels, this machine learning algorithm will suggest the best possible ways to cleanse the ocean, this can be done because the machine learning algorithm has already included the database of all the available past pollution history (we assume), therefore by using the regression model, it can then give us the prediction of the best ways to tackle it.
We believe these applications of machine learning can inspire a lot of countries to adopt because clean marine life is statistically proven to enhance the revenue of the country by tourism. A study in Orange County, California, found that reducing marine debris at beaches by even 25 percent could benefit residents by $32 million from increased summer tourism and recreation. Therefore, this machine learning model's value is undoubtedly pragmatic and practical. It not only can return a healthy marine ecosystem to nature. At the same time also provides respective countries with a lucrative amount of revenue.
Space Agency Data
Coast NOAA government
Hackathon Journey
Experience:
Happy.
What did we learn:
Teamwork makes the dream work.
What inspired us to choose this challenge?
We care for our environment; climate change has been a critical topic for a long while, it's quite saddening to always happen to see a lot of natural disasters news on social media or newspapers. The earth is sick, and humans are responsible for it. Therefore, when our group scans through the challenges available on the website, this topic resonates with each of us the most.
Our approach to developing the project
We use a machine-learning algorithm to develop a model to tackle the problem. While the satellites scan through the ocean, it is obtaining data of the ocean, and this data will then be included in our database and be used by our machine learning algorithm model. Only by knowing the pollution level of the ocean, authorities could then formulate a strategic method to cope with it.
How do we resolve setbacks and challenges
We talked to each other.
We face setbacks.
We find solutions.
If we can't find the solution, we pretend it is not there.
We would like to thank NASA for providing us with this opportunity to know what we don't know.
References
Coast NOAA government
Tags
#satellites #marinelife #climatechange #water #pollution #plastic
Global Judging
This project has been submitted for consideration during the Judging process.