Harnessing AI for Sustainability: Applied AI and Machine Learning Algorithms for Air Quality Prediction

The sustainability of ecosystems and human well-being are both directly impacted by air quality, which is a crucial component of environmental health. Due to its negative effects on societal advancement, the environment, and public health, the deteriorating air quality around the world has sparked serious worries. It is essential to have accurate and fast air quality forecasts in order to address this urgent problem since it can offer helpful information for making wise decisions, carrying out mitigation strategies successfully, and protecting sensitive communities. Using AI and the linear regression technique, we will investigate many facets of air quality forecasting in this study. To guarantee the dependability and quality of the dataset, we will examine data gathering, preprocessing, and feature engineering strategies. We will also go over selection criteria and compare AI algorithms, emphasizing the benefits of Linear Regression over alternative approaches. We will also give a thorough explanation of the Linear Regression algorithm, complete with a pseudocode that fits the Vinnytsia air quality dataset’s particular context.