Abstract:

The ability to forecast and analyze air quality has significantly improved over time. We previously placed a great deal of reliance on conventional techniques like statistical models and simple equations. These methods, however, found it difficult to convey the dynamic and intricate character of air pollution. In an effort to enhance air quality forecasts, scientists and researchers have been using artificial intelligence (AI), machine learning, and big data analytics. Conversely, air pollution is a major worldwide problem that impacts not only our surroundings but also our health and welfare. Additionally, it is connected to cardiovascular and respiratory conditions, which raises the number of illnesses and fatalities. Precise forecasts of air quality enable public authorities, governments, and people to respond promptly in order to mitigate pollution, protect public health, and enhance urban development. We require precise study and forecast of air quality in order to address this urgent issue. The shortcomings of conventional approaches for predicting air quality are what drove us to create this AI model. As we've shown, these approaches frequently lack precision and have trouble taking into account the many variables driving air pollution. With its capacity to analyze enormous volumes of real-time data and spot intricate patterns, artificial intelligence (AI) presents a promising way to improve the precision and dependability of air quality forecasts. As a result, this work presents a cutting-edge Artificial Intelligence (AI) model that is intended to accurately and efficiently forecast and assess air quality. This model attempts to satisfy the increasing need for trustworthy real-time air quality information by combining state-of-the-art AI algorithms and data analytics methodologies.

Description:

.