doguilmak/sunspot-rnn-forecast

Advancing sunspot forecasting with Recurrent Neural Networks for precise predictions in space weather research and monitoring.

Sunspot Forecasting with Recurrent Neural Networks (RNNs)

Picture Source: csmonitor

Abstract

In the realm of space weather, the prediction of solar phenomena plays a vital role in safeguarding technological assets on Earth. Sunspots, as indicators of solar activity, have implications for radio communication, navigation systems, and power distribution. With an increasing dependence on technology, accurate forecasting of sunspot activity becomes essential. Traditional methods often struggle to capture the intricate patterns in solar data, motivating the exploration of advanced machine learning techniques. Recurrent Neural Networks (RNNs) excel in modeling sequential data, making them well-suited for the temporal nature of sunspot activity. This study focuses on harnessing the capabilities of RNNs to enhance sunspot forecasting accuracy, contributing to the resilience of Earth's technological infrastructure against space weather events.

Context

In recent years, advancements in space technology and satellite observations have provided an abundance of sunspot data. Sunspots, dark areas on the Sun's surface, are indicative of strong magnetic activity and influence solar radiation. Accurate forecasting of sunspot activity is crucial for anticipating potential impacts on Earth's technology-dependent systems. Traditional forecasting methods often fall short in capturing the dynamic and evolving nature of sunspot patterns. Leveraging machine learning, particularly RNNs, offers a promising avenue to improve the precision and reliability of sunspot predictions. This project explores the application of RNNs to forecast sunspot activity, providing valuable insights for space weather monitoring.

Dataset

Our analysis is based on the WDC-SILSO, Royal Observatory of Belgium, Brussels (Yearly total sunspot number) that provides comprehensive data on sunspot activity. For additonal information, contact Laure Lefèvre (Email: [email protected]) Royal Observatory of Belgium Av. Circulaire, 3 - B-1180 Brussels, Belgium

Project Overview

This project is designed to achieve the following objectives:

• Data Preprocessing: Collect and preprocess historical sunspot data to ensure quality and consistency.

• Model Architecture: Develop a deep learning model using RNN layers to effectively capture temporal dependencies in sunspot activity.

• Training and Validation: Train the RNN model on a subset of the data and validate its performance.

• Forecasting: Utilize the trained model to make accurate sunspot activity forecasts.

• Evaluation and Interpretation: Evaluate the accuracy of the model's predictions and draw insights to enhance our understanding of solar dynamics.

Requirements

• Python 3.x
• TensorFlow
• NumPy
• Pandas
• Matplotlib

Usage

1. Clone the repository to your local machine.
2. Preprocess the sunspot data using the provided scripts.
3. Train the RNN model using the training data.
4. Use the model for sunspot activity forecasting.

Contributing

Contributions to this project are welcome. You can contribute by improving data preprocessing, optimizing the model, or enhancing forecasting capabilities.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact Me

If you have something to say to me please contact me:

• Twitter: Doguilmak
• Mail address: [email protected]