Air Pollution Prediction Using Machine Learning and Neural Network

With increased industrialization and urbanization, air pollution poses an environmental threat. Today, air quality is crucial for society and the environment. Due to increased air pollution, which has become a significant problem globally, accurate air pollution forecasting and prediction have become challenging and essential jobs. There are various numerical and statistical tools for predicting and analyzing the state of the air. However, for air quality prediction, the artificial neural network (ANN) is an effective data processing tool with high classification accuracy, a strong capacity for parallel processing, and a robust data learning ability to store data in a disturb Further more. As a result, this paper focuses on a survey review of recent air quality prediction techniques. Furthermore, it highlights the essentials.

I. Introduction

In recent years, predicting the effects of air pollution, which can have a significant adverse impact on air quality, atmospheric visibility, climate change, and public health, is problematic because it varies depending on the terrain, weather, and anthropogenic factors and is among the most harmful things in the world [1].