How Can Deep Learning Be Applied to Predict Solar Flares?

Introduction

What are solar flares? The answer to this is “solar flares are the energy which has been ejected suddenly on the sun’s surface.” These solar flares have made our scientists and astronomers wonder for decades now. These sudden ejections of energy from the sun’s surface can cause disturbances in our satellites, GPS communications, and even the power supply on the Earth. Predicting the peak time of solar flares for scientific experiments and our smooth existence on Earth has become necessary. Now, these studies of solar flares include the study of solar physics, astrophysics, and complex techniques. This is not only for celestial objects but also to predict these solar flares, which might have unexpected erections.

Now, as we were talking about predicting solar flares, we can use deep learning for this purpose, as it is a part of machine learning and an excellent tool for making predictions and analyses. By taking a large solar flare dataset, we can create a model that can predict the peak time of a solar flare by learning through the dataset. Using this model, we can set an example of how to use these complex deep learning models for these types of advanced predictions and understand the solar activity concepts easily.

Learning Objectives

• From this, we will learn the impacts of solar flares on Earth and the fundamentals of solar flares.
• We’ll understand how to apply deep learning models to solar flare predictions and grasp their underlying principles.
• We will learn how to analyze solar flare data and understand the process for exploratory data analysis.
• We will learn how to develop the deep learning model to predict the occurrence of solar flares.

This article was published as a part of the Data Science Blogathon.

The Nature of Solar Flares

Solar flares are events in which an abundant amount of energy will be released during the event, which will be almost equal to the energy released by burning lakhs and lakhs of nuclear bombs. The reason these flares occur is the release of magnetic energy, which will be stored in the atmosphere of the sun. These flares can have many impacts on Earth. For example, a flare with less frequency or power will cause less effect on Earth. In contrast, a flare with high frequency and intensity will cause significant effects, such as storms of geomagnetic, which affect communication and power supply and satellite communications.

As we know the study of these flares might be interesting but it is also a little hard to predict the major effects caused by these flares on the latest technology. As we study more on these solar flares we will get to know the challenges in predicting these flares and spatial events.

Deep Learning in Solar Flare Prediction

Deep learning can operate massive data and learn the patterns and flow from that large data, which will help us predict the peak time of the solar flare. We can use Recurrent Neural Networks(RNNs) to understand solar data, which includes phantom information related to solar activity, which will help us to predict the peak time of the following possible occurrence of solar flare. To achieve this, we need to train our model on a large dataset through which our model can analyze the patterns and other information and predict the peak time of the next solar flare.

We know that the accuracy of models depends on the quality and quantity of the data that will be used for training them. So, the selection of this dataset will play a prominent role in developing the best prediction model. In the next section of this blog, we will learn about the dataset we will be using, which is “Solar Flares from RHESSI Mission.” 

About Dataset

Before we explore the dataset, we will load the dataset and understand how the structure of solar flare is distributed and try to analyze the content.

Now in this part, we will find out the basic understanding that we will get to know from the dataset, like:

• To predict peak time, we need some initial points or features such as the start and end time of the solar flare, the basis of classification of the solar flare, the peak time of the solar flare, and other relevant features.
• We can get to know the brightness of the flare in the X-ray band of the flare which will be classified as C, X, and M.
• The dataset covers the frequency and timing of solar flares, essential for identifying patterns and cycles.

Time Series Data

Time series data consists of data points collected at specific time intervals. In this experiment of predicting the peak time of solar flare occurrence, time series data will also calculate the other features that occurred, such as the number of sunspots, strength of the sun’s magnetic field, and solar irradiance. In this data type, it is difficult to understand the changes in solar activities to predict the peak time of solar flare occurrence.

Characteristics and Importance

• Sequential Nature: This time series data will reveal the sequential nature of the activity, which will help the researchers understand its complex patterns and styles.
•  Predictive Power: Using time series data in our deep-learning model will help researchers predict upcoming events based on the information gained by previous events, especially in networks like LSTM.
•  Handling Temporal Dependencies: This technology of time series data will help us manage the dependencies that impact previous behavior to present behavior of data and auto-correlations, which helps predict the accurate value of solar flare occurrence.

Through these points, we can start preprocessing our data, such as normalizing the data points, handling the missing values, and encoding the categorical data if necessary. This understanding of the data will lead us to build the most relevant model to predict the peak time of the solar flare.

import pandas as pd

data = pd.read_csv('/kaggle/input/solar-flares-rhessi/hessi.solar.flare.2002to2016.csv')

data.head()

Output:

Data Preprocessing and Preparation

To train deep learning models effectively, we preprocess the data to format the dataset correctly. In this step, we will first clean the data, which is nothing but handling the missing values. Then, we will create some new features that will help us predict the peak time of the solar flare. Finally, we will scale the feature of the data and split it into training and testing data.

Now let’s see how to implement these steps in the code.

Step1: Importing libraries

import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
from tensorflow.keras import layers

Step2: Converting to Datetime

data['start_datetime'] = pd.to_datetime(data['start.date'].astype(str) + ' ' + data['start.time'])
data['end_datetime'] = pd.to_datetime(data['end'])
data['peak_datetime'] = pd.to_datetime(data['peak'])

In this step, we are converting the start date, start time, end time, and peak time to the DateTime object in our dataset. This helps in the precise prediction of our model.

Step3: Scaling Relevant Features

features_to_scale = ['duration.s', 'total.counts', 'x.pos.asec', 'y.pos.asec']
scaler = MinMaxScaler()
data[features_to_scale] = scaler.fit_transform(data[features_to_scale])

In this step, we are scaling the selected features to the range between 0 and 1 which helps our model in the training step.

Step4: Extracting Time Features

def extract_time_features(dt):
    return dt.hour, dt.minute, dt.second

This function helps us get the specific hour, minute, and second from a date and time. It’s helpful because it breaks down the date and time into smaller details, which can make our computer models work better by giving them more specific time information.

Step5: Applying Time Feature Extraction

data['start_hour'], data['start_minute'], data['start_second'] = 
zip(*data['start_datetime'].apply(extract_time_features))
data['end_hour'], data['end_minute'], data['end_second'] = 
zip(*data['end_datetime'].apply(extract_time_features))
data['peak_hour'], data['peak_minute'], data['peak_second'] = 
zip(*data['peak_datetime'].apply(extract_time_features))

When you use the extract_time_features function on your datetime columns, it adds new columns to your dataset that show the hours, minutes, and seconds of the start, end, and peak times. This helps the model understand and find patterns in the data more easily.

Step6: Selecting Features and Target for the Model

X_columns = ['duration.s','total.counts', 'x.pos.asec', 
'y.pos.asec', 'start_hour', 'start_minute', 'start_second', 'end_hour', 'end_minute', 'end_second']
y_columns = ['peak_hour', 'peak_minute', 'peak_second']

Identifying which columns of the dataset will be used as input features (X_columns) and which ones will be predicted (y_columns). This distinction is essential for setting up your model architecture and training process correctly.

Step7: Reshaping Input Features and Preparing Target Variables

X = data[X_columns].values.reshape(-1, 1, len(X_columns))
y = data[y_columns].values

Now, we have cleaned the data and added the necessary features to predict the peak time of the solar flare. I normalized the data to ensure every point is on the same scale, crucial for model training.

Data preprocessed, we build a deep learning model to predict solar flare peak times using added features.

Model Building

This section will construct a model using libraries such as Keras and tensor flow. We predict solar flare categories (C, X, M) based on added features. Here, “C” denotes small flares, “M” medium, and “X” significant solar flares.

Long Short-Term Memory (LSTM)

To overcome the challenges of learning long-term dependencies, a specialized form of recurrent neural network(RNN) called LSTM was introduced. The vanishing gradient problem prevents RNNs from remembering long sequences of information.

LSTM consists of an input gate, output gate, forget gate, and memory cells. These components will help the LSTM remember its importance. Network and store them in the components and discard the unnecessary information to save the storage space. LSTM’s ability to handle long patterns in Time Series data aids in accurate predictions.

Building the Deep Learning Model

Our model consists of many hidden layers, input, and output layers. In the input layer, the model will accept the features of solar flares as input.We added an optimizer like ‘Adam’ to our hidden layers to help us understand the relationship between dependent and independent variables, as well as the complex patterns present in the dataset. We use the softmax activation function in the output layer to classify the flares into their respective categories.

Now let us use tensorflow and build our model:

def build_lstm_model(input_shape):
    model = keras.Sequential([
        keras.Input(shape=input_shape),
        layers.LSTM(64, return_sequences=True),
        layers.Dropout(0.2),
        layers.LSTM(32),
        layers.Dense(3)  # Predicting hour, minute, second
    ])
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model

input_shape = (X.shape[1], X.shape[2])
model = build_lstm_model(input_shape)

After this, we need to train our model using the training data. In this process, we will manage the weights of the model.

Training the Model

# Training the model
history = model.fit(X, y, epochs=20, batch_size=64, validation_split=0.2)

Output:

In this code, we have trained the model with 20 epochs of data split to 20 percent of testing data. This helps us test the model and change the requirements accordingly.

Making Predictions

After the model is trained, we use it to predict the new data. We input previous solar flare peak time features, and the model predicts the next peak time.

Function predict_peak_time

def predict_peak_time(model, scaler, user_input):
    scaled_features_input = scaler.transform([[user_input[key] for key in features_to_scale]])

    # Adjusting unscaled_features_input to make it a 2D array by adding an extra dimension
    unscaled_features_input = np.array([user_input[key] for key in ['start_hour', 
    'start_minute', 'start_second', 'end_hour', 'end_minute', 'end_second']]).reshape(1, -1)

    # Now both arrays are 2D and can be combined using np.hstack
    X_user = np.hstack([scaled_features_input, unscaled_features_input]).reshape(1, 1, -1)

    # Now 'X_user' should have the correct shape and number of features
    predicted_peak_time = model.predict(X_user)

    return predicted_peak_time

Output:

The model accurately predicted a peak time of 21:34:29 for the input (Start time: 21:29:56, End time: 21:41:48).

Conclusion

Employing deep learning for predicting peak solar flare times is a significant advancement in space weather forecasting. By looking at lots of information and finding patterns, these models can help us get ready for solar flares. This could help us prevent problems with things like phones, electricity, and satellites. These models help us prepare for solar flares by analyzing extensive datasets and identifying patterns. This will help lessen the problems they cause for things like phones, electricity, and satellites.

Key Takeaways

• We have learned the importance of predicting the peak time of solar flare and their impacts on the Earth.
•  We also learned how to implement the deep learning model to predict the peak time of solar flare.
•  We learned how to prepare our data for deep learning and why certain steps are important before using it.
•  We learned how to build, evaluate, train, and test deep learning model for predicting peak time of the solar flare.

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