Impact of Deep Learning in Analytics of Climate Change: A Survey

Rohith R, Geetha G

Abstract: The paper Impact of Deep Learning in the Analytics of Climate Change surveys the recent advances and applications of deep learning methods for various climate change related tasks, such as detection, prediction, estimation, and attribution. The paper reviews the literature on how deep learning can help with super-resolution, pattern recognition, forecasting, and data fusion in global warming, air pollution, drought, wildfire, and other environmental issues. The flow of our survey starts with reviewing the articles on causes then to consequences and finally the remedies of climate change. The paper also discusses the challenges and limitations of deep learning for climate change analytics, such as data quality and availability, model interpretability and robustness, and computational resources and efficiency. The paper concludes that deep learning is a promising branch of machine learning that could provide valuable insights and solutions for climate change mitigation and adaptation.

Keywords: Deep Learning, Climate Change, Analytics, Global Warming, Environmental Issues, Mitigation and Adaptation