Wildfire Alert System

Inspiration

-The constant threat of wildfires in California inspired us to find a way to notify as many people as possible, of potential dangers. Using live tweet data from Twitter, we want to get information out before it's too late for anything.

What it does

-It scrapes live data from the twitter API, data of which is being used by IBM Watson's natural language processing unit to decide if the tweet is real or irrelevant and then send text messages about the data to a phone number as an Alert notification.

How we built it

-We used python to implement and use different APIs like Tweepy and using IBM Watson API and created a cohesive code structure that calls in these different APIs, as well as streaming the live data from twitter, and then use Twilio to take in phone numbers and sending them text messages from the data we pulled, using a python created GUI.

Challenges we ran into

-The most challenging part was implementing IBM Watson's natural language into our code from it's cheer complexity and then actually using the data to create terminating conditions to exclude irrelevant tweets from the live data.

Accomplishments that we're proud of

-Figuring out how to use IBM Watson's natural language was our biggest accomplishments and understanding it's data, as well.

What we learned

We learned how to write in python, how to use APIs, how to use IBM Watson, send text messages, create GUI using python for applications.

What's next for Wildfire Alert System

Be able to implement more than just Wildfire, depending on the county that would use such code, and their most common problems.

Built With

• ibm-watson
• python
• tweepy
• twilio