GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality.