AI-Powered Solar Sensor for Hurricane Ident. and Prediction

Inspiration - Inefficiency in hurricane prediction of category strength and direction.

What it does - Tracks wind speed and sends sensor data to a microcontroller that will compute it and send it to a database to be used to optimize hurricane identification and prediction.

How we built it

The hardware is a device that uses an integrated anemometer and a ultrasonic sensor to collect data on the strength of wind speed. The device would used in a system with multiple devices distributed across a specific region to collect data with that region

The software has been developed to efficiently process a set of hurricane data with the variables Wind Speed, Hurricane pressure, Latitude, and Longitude. Based off these variables a machine learning algorithm has been developed to increase the efficiency and accuracy of hurricane identification and trajectory prediction

The anemometer and ultrasonic sensor are being implemented on the cubeStat nanosatellite which will encompass a system of sensors and satellites in orbit to continuously collect and deliver data back to the ground. Once the data is obtained the software will process and compute a predicticted position

Challenges we ran into

We ran to issues with the availability of hardware, as the hardware we looking in to wasn't physically available, so we used an online simulator to combat this, and are using it to display our project.

Accomplishments that we're proud of - Completing both the hardware and software aspects in a short amount of time.

What we learned - How to use Wokwi, ESP32, C++ language

What's next for AI-Powered Solar Sensor for Hurricane Ident. and Prediction - Satellite that uses computer vision to calculate wind speed using cloud movement and position.

Built With

• ai
• blender
• c++
• engineering
• fusion
• github
• machine-learning
• matlab
• python
• vectary