Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and in field to show if there is a relationship between changing market concentration various pollutants. This aims find Saudi Tadawul All Share Index (TASI) multiple pollutants, including particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur (SO2), carbon monoxide (CO), AQI. Based on tree models, created linear regression two prediction Chi-square Automatic Interaction Detection (CHAID), CR-Tree. In order achieve target this research, TASI dataset relates six pollutants time; afterward, new divided into three parts—test, validate, train—after eliminating outlier data. test performance R2 error functions are used. model results found that PM10, NO2, CO, month, day, year significant, whereas O3, SO2, AQI indices insignificant. showed scores 0.995 0.986 for CR-Tree CHAID, respectively, with RMSE values 387 227 respectively. models CHAID performed better than because it used only indices, namely, AQI, month. indicated an O3.