Our team developed an application that would help low-income communities respond to disaster better by creating a monitoring system that prioritizes rural areas as high-risk.
This venture is essential to society because it is inclusive to those in rural areas to help prepare for devastating natural disasters like that of floods.
Additionally, through machine learning, the application is also capable of improving by receiving feedback and reports from users. It would also include collaboration with other organizations that would be willing to provide aid through relief goods, evacuation centers, etc. With further improvement and implementation, this application would be able to play a big part in revolutionizing the way the Philippines handles one of the most prevalent natural disasters.
A negative association between income per capita and natural disaster risk measures has been found in recent empirical literature, supporting the logic that higher incomes allow countries to minimize the risk of disasters. With this, there is a need to label low-income areas as high risk especially to heavy typhoons.
With the added negative impact of the pandemic, our team felt as though the Philippines is in dire need of assistance when it comes to mitigating the effects of floods.
The application was created through a web application called Thunkable. With limited resources, the team was able to create the application but the machine learning component was not yet tested due to technical limitations.
As for the problems, the team’s lack of equipment capable of supporting the ambition of the project may have been the most notable hindrance. There were also instances of connectivity issues which led to further setbacks. Rest assured, the potential of this project is unmatched when it comes to benefiting the masses and it will surely contribute to positive global change.
For this project, we were able to utilize satellite imagery from NASA in order to predict the poverty levels in an area. In order to ensure the safety of every citizen in a sample population (e.g. a barangay), the app would allow the appointed authorities (e.g. the barangay tanod) to track the attendance of the citizens in order to make sure none are excluded.
Video Demo: http://bit.ly/MarAbeVideo