The aim of this research is to predict the ozone concentration level for the next three days. Linear regression model and a nonlinear regression model are used to measure the air pollution data and the result was compared. The performance indicator used to evaluate the accuracy of the methods is Index of Agreement (IA), Prediction Accuracy (PA) and Coefficient of Determination (R2). While Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) are for error measured. The results show that the prediction for the next three days. The highest ozone concentration of the linear regression model is 0.085ppm at Petaling Jaya, Selangor. While the lowest concentration for the linear regression model is 0.015 ppm at Klang, Selangor. Besides, the highest ozone concentration for the nonlinear regression model is 0.1 ppm at Petaling Jaya, Selangor for the second-day prediction. Comparison between the linear regression model and a nonlinear regression model indicates that nonlinear regression model can as an alternative method to the linear regression model.
|Journal||IOP Conference Series: Materials Science and Engineering|
|Publication status||Published - 14 Aug 2019|
|Event||International Conference on Green Engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019 - Bangkok, Thailand|
Duration: 04 Feb 2019 → 05 Feb 2019
All Science Journal Classification (ASJC) codes
- Materials Science(all)