Inspiration
Following the theme of nature, we realized two of the members in our team were raised in areas that were commonly prone to sinkholes. From this, we were inspired to create a program that predicts whether or not an area is susceptible to sinkholes. This information can be used to determine if a location is acceptable for construction or if an area should be evacuated, etc.
What it does
Our Early Detection for Sinkholes takes the location of the user and reads the information about the area from a database to determine if it's at risk for sinkholes.
How we built it
To start, a fake data set with 10 cities was created in an Excel spreadsheet. Then, a backend python server was made to pull data from the spreadsheet and determine if the city is at risk for a sinkhole - this was created in visual studio code using Flask to post receive data. A front end JavaScript app was created using React - this prompts the user for the input city and sends it to the backend.
Challenges we ran into
The main challenge we ran into during this project was connecting the front end to the back end server. There was an issue connecting them to the same local host and we were not able post request and/or post receive data.
Accomplishments that we're proud of
Despite our challenge and not fully making the code run, we're proud of ourselves for learning a new library (React js) and web framework (Flask). As a team of fairly new coders, getting as close as we did to developing a full stack program is a great accomplishment.
What we learned
We learned how to use various new frameworks and platforms, such as React and Flask, and the basics of creating a front end and back end.
What's next for Early Detection for Sinkholes
We hope to continue working on this program outside of the hackathon. Our first step would be to figure out how to connect the front and back end. From there, we would grow the database to include every city in the nation, and possibly eventually extend to around the world. We hope the program can one day make use of satellite and radar images/data by reading it into the database and using it to predict sinkholes (this would include soil movement, foundation cracks, dramatic changes in groundwater, etc.).
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