Prediction of Future Ozone Concentration for Next Three Days Using Linear Regression and Nonlinear Regression Models

Joint Conference on Green Engineering Technology & Applied Computing 2019

工业技术（总论）;计算机科学

Mubin Zahari, Nazirul^1 ; Ezzah Shamimi, Raja^2 ; Hafiz Zawawi, Mohd^1 ; Zia Ul-Saufie, Ahmad^2 ; Mohamad, Daud^3

Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Jln IKRAM-UNITEN, Selangor, Kajang

43000, Malaysia^1

Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Jln IKRAM-UNITEN, Selangor, 43000, Malaysia^2

Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, Pulau Pinang, Malaysia^3

关键词: Coefficient of determination;    Index of agreements;    Linear regression models;    Nonlinear regression models;    Normalized absolute errors;    Performance indicators;    Prediction accuracy;    Root mean square errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/551/1/012006/pdf
DOI  :  10.1088/1757-899X/551/1/012006

来源: IOP

【 摘 要 】

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.