Air Quality Index (AQI) Prediction using Automated Machine Learning with TPOT-ANN*

Pollution is a critical and disturbing problem that people encounter daily in today's world and also has an impact on the quality of air. The issue is so crucial that it cannot be overlooked and its effects are felt everywhere. The climatic variables that affect the AQI, such as NO2, NH3, SO2, CO, O3, fog ,temperature, smoke, dew, mist, benzene, toluene, xylene, etc. The AQI measures the severity of the pollutants present in the air. It classifies the severity of air quality into six categories, each with its own range of values. The categories are as follows: Good, which ranges from 0–50 on the AQI scale, indicating that the air quality is generally safe and healthy for everyone to breathe. Moderate, which ranges from 51–100 on the AQI scale, indicating that the air quality is acceptable but may pose a moderate risk for certain individuals, such as those with respiratory issues. Unhealthy for Sensitive Groups, which ranges from 101–150 on the AQI scale, indicating that the air quality is dangerous for certain individuals, for example the youth or the younger and older ones or people having respiratory problems. Unhealthy, which ranges from 151–200 on the AQI scale, indicating that the air quality is hazardous and can cause serious health problems for everyone. Very Unhealthy, which ranges from 201–300 on the AQI scale, indicating that the air quality is extremely dangerous and can cause severe respiratory and cardiovascular problems. Hazardous, which ranges from 301 and higher on the AQI scale, indicating that the air quality is life-threatening and can cause serious health problems even for those who are otherwise healthy. Overall, the AQI is an essential tool for assessing the severity of air pollution levels and determining the appropriate measures that need to be taken to protect public health. The suggested model aims to evaluate the air quality. The proposed model suggests a strategy for measuring future AQI data from the present and historical AQI data by using automated machine learning techniques. Threshold value might be specified as a similar parameter since TPOT increases the iterations in number, which increases the depth of the node. The data on air pollutants is obtained from the sensors, processed according to a single schema, and then saved as a dataset. This dataset has undergone many preprocessing operations, including normalization, discretization and attribute selection. The machine learning system would learn from the data (pertaining to point in the time) and database to offer the user with comparable statistics to minimize processing time and increase platform efficiency.