📄 Abstract
Climate change is defined as a long-lasting change in the mean weather patterns found on Earth. These changes are either natural, or human made. Since the 19th century, climate change has been on the rise. This is mostly due to igniting fossil fuels like gas, coal, and oil. These can lead to various problems like food and water scarcity, disease, increased flooding, extreme heat, and economic loss. It can also cause the sea levels to rise, and make oceans warmer and more acidic, and escalate human migration. It is referred to as the most significant threat to health on a global scale by the World Health Organization. Even if the efforts made to reduce future warming is successful, some effects will continue for tens of decades. This paper attempts to forecast climate change based on the changes in temperature across the globe from the 19th century onwards. A deep learning-based time series model is built using Recurrent Neural Networks (RNNs) to forecast the average temperature. This model will attempt to predict future values based on historical data.
🏷️ Keywords
📚 How to Cite:
Hari Babu Guraka , CLIMATE CHANGE FORECASTING USING DEEP LEARNING , Volume 12 , Issue 7, october 2024, EPRA International Journal of Climate and Resource Economic Review (CRER) , DOI: https://doi.org/10.36713/epra18552