High-Level Project Summary
We developed a logistical regression model to classify and analyze datasets to observe possible increase in magnetic field density and compared them with threshold values obtained from previous DSCVR datasets of NASA to predict the possible occurrence of a Carrington event. This mechanism would involve updating the magnetic field, proton density and temperature data based on ready availability of latest data from NASA. We also looked into possible communication techniques using cognitive radios during the Carrington event to send alerts.
Link to Final Project
Link to Project "Demo"
Detailed Project Description
The project Cognitive Alerting System takes the datasets of DSCVR from NASA time to time and determines if there is a chance of occurrence of Carrington event by comparing the magnetic fields in datasets to the threshold value obtained by the datasets of previous years. It works by using regression and statistics.
This is beneficial as we can avoid large time delays in training neural networks from time to time and provides a simple solution to determine solar flares whenever the datasets are released by DSCVR.
Websites used: WIX.com
Coding Languages: Python
Software: Google Colab, Filmora
Space Agency Data
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Hackathon Journey
NASA space apps hackathon indeed offered a wide range of experiences to all of us participants who wanted to learn, network, grow and build something impactful in the process.
References
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