AQI And Occurrence of Acid Rain Prediction UsingMachine Learning

Abstract

Significant environmental problems like air pollution and acid rain have an impact on bothecological systems and human health. The Air Quality Index (AQI), which measures theamount of pollution in the air, is an essential air quality indicator. Because of atmospheric contaminants like Sulfur dioxide and nitrogen oxides, acid rain is defined as precipitation with enhanced acidity. These contaminant shave negative side effects that impact aquatic life, crops, human respiratory health.  The ultimate objective of our project is to develop a machine-learning module that can forecast the AQI and the occurrence of acid rain. We acquire information from a variety of sources, such as weather stations and existing air quality data, which we meticulously preprocess to determine the key variables for predicting the AQI and Acid Rain. We train the module using a Deep Learning model, then assess its effectiveness. Our findings show that the module successfully foretells the AQI and Acid Rain. Using well-known measures like R-squared and Mean Squared Error, we evaluate its performance. Theresults show that the factors that have the most influence on forecasting AQI and Acid Rainare temperature, humidity, precipitation, ozone, nitrogen dioxide, and Sulfur dioxide. We further show that the module’s performance is stable across several test data sets, suggesting that it could be useful in practical applications. Our approach demonstrates the immensepotential of artificial intelligence and machine learning in monitoring the environment. Our module can be extremely helpful for policy makers and ecological organizations in identify in glocations that need targeted measures to enhance air quality and lessen the likelihood of AcidRain by giving accurate and reliable predictions of the AQI and Acid Rain. In the end, our initiative emphasizes the significance of continued research in this crucial area and highlights the value of data-driven techniques in addressing complex environmental concerns.