Applications of machine learning and deep learning methods for climate change mitigation and adaptation

Climate change is a global issue that must be considered and addressed immediately. Many articles have been published on climate change mitigation and adaptation. However, new methods are required to explore the complexities of climate change and provide more efficient and effective adaptation and mitigation policies. With the advancement of technology, machine learning (ML) and deep learning (DL) methods have gained considerable popularity in many fields, including climate change. This paper aims to explore the most popular ML and DL methods that have been applied for climate change mitigation and adaptation. Another aim is to determine the most common mitigation and adaptation measures/actions in general, and in urban areas in particular, that have been studied using ML and DL methods. For this purpose, word frequency analysis and topic modeling, specifically the Latent Dirichlet allocation (LDA) as a ML algorithm, are used in this study. The results indicate that the most popular ML technique in both climate change mitigation and adaptation is the Artificial Neural Network. Moreover, among different research areas related to climate change mitigation and adaptation, geoengineering, and land surface temperature are the ones that have used ML and DL algorithms the most.

Automated summary

This study explores the most popular machine learning (ML) and deep learning (DL) methods applied in climate change mitigation and adaptation, and identifies common mitigation and adaptation measures through word frequency analysis and topic modeling. The results reveal that Artificial Neural Network is the most popular ML technique, and geoengineering and land surface temperature are the areas that have utilized ML and DL algorithms the most in relation to climate change.