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Develop a machine learning algorithm to correlate the data collected by the GMAG and ASI laboratories to :
1. Improve the detection of auroras in severe weather conditions by using GMAG data as a input for probability measures.
2. Improve the forecasting of auroras through the forecasting on the time series of GMAG data.
3. Develop probabilistic method ( Bayesian trees ) to generate the probability for an aurora occurrence given current and past magnetometer readings, as well as data about past aurora observations.
1. Time Forecasting methods such as ARIMA, SARIMA, SARIMAX, SARFIMA.
2. Various neural network models such as LSTM, GRU, BiLSTM, Auto-encoder decoders etc.
3. Statistical methods for data correlations.
Single member, Abhishek . : 3rd year undergraduate student in Computer Science-Physics, Research Analyst in Machine Learning and Data Science.
Very large datasets which could not be stored locally. To reduce time and effort, used subsets of the given datasets in order to build and test the machine learning models.
Long training times for neural network models due to local machine limitations on GPU memory. Overcame by reducing the network sizes to accomodate training times.
1. CSA THEMIS Datasets : GMAG and ASI measurement for the year 2017-2018
Language - Python 3.6
Package Environment - Anaconda Package Distribution
Libraries used - Keras, TensorFlow, Matplotlib, Pandas, Numpy, os.
IDE - Spyder 3.6
Notebooks - Jupyter Notebooks 3.6
Future steps :
Complete the implementation of Bayesian networks for generating probabilistic output for forecasting the probability on an aurora occurring in the next t timestamps given the current GMAG sensor values.
SpaceApps is a NASA incubator innovation program.