High-Level Project Summary
The challenge we worked on is" save the earth from another Carrington event" , our solution is to build a machine learning algorithm for the DSCOVR spacecraft's FC instrument to track and follow the changes in the peak solar wind speed that will help us to predict the occurrence of the Carrington event again based on the information that was recorded by The Deep Space Climate Observatory to provide an early warning for solar stormThe data set we will depend on is the data that recorded for the the corona region of the sun by Parker solar probe ,the changes that occur in the coronal mass ejection are good and strong indication for the solar storm and display these predictions in mobile app
Link to Final Project
Link to Project "Demo"
Detailed Project Description
It is possible to reduce the losses caused by solar storms if they are predicted before they occur in such a way that scientists determine the extent of their severity and the areas and limits of their impact. The solution we offer is to build a machine learning algorithm for the DSCOVR spacecraft's FC instrument to track and follow the changes in the peak solar wind speed that will help us to predict the occurrence of the Carrington event again based on the information that was recorded by The Deep Space Climate Observatory (DSCOVR) and to train the machine and give sufficient information about the solar storm such as its vehicle velocity, thermal velocity and ion density. We can use the information that was recorded by parker solar probe about the chemical interaction of the outer surface of the sun which will give us a good illustration about the regions of intense heat and solar radiation, this ensures that there is enough time between the emergence of the solar storm and it's hitting the Earth. And to increase the volume of data that will be relied upon to confirm the forecast, we can use the data on the change in the spectrum emitted by the sun, which reaches the radio wave band, which is a strong indicator of solar storms. And to display these predictions about the timing, speed and ion density of the solar storm in a way that facilitates dealing with this data, we will build a mobile application to display the results of the machine learning algorithm .
briefly our idea that is to make Dataset for a specific Elements After We know the Sun core elements and its properties such as speed of Solar wind and ion density and so on , After that we will have an accurate details that make us predict with an accurate scale and we get this data from Nasa such as parker Solar probe and faraday cup and if we use this way we will predict the solar wind details such as its time and its speed .
We seek to get benefits of current
Space Agency Data
We Have Met a Technical Issue That We Can Not open the RESOURCES That Nasa open Source (We Can not open cdf Files and we asked 3 MENTORS To Help us but we do not know how to open this files right now ) , But we used online sites such as ACE Space craft - OMNI Web Nasa Data - Space Whether prediction Center
Hackathon Journey
- teamwork
-brainstorm
-how to do meetings well
-work under pressure
- Networks
-different cultures
-new colleagues
-Improve our Career
References
Index of /pub/data/dscovr/h0/mag/2022 (nasa.gov)
DSCOVR: Deep Space Climate Observatory | NESDIS (noaa.gov)
DSCOVR Space Weather Data | NCEI (noaa.gov)
https://solarsystem.nasa.gov/missions/ace/in-depth/
https://omniweb.gsfc.nasa.gov/
Tags
AI , ML , NN , Mobile Applications