Abstract:
The increasing problem of air pollution has led to the advancement and improvement of air quality prediction studies. Predicting air quality in advance is crucial for mit...Show MoreMetadata
Abstract:
The increasing problem of air pollution has led to the advancement and improvement of air quality prediction studies. Predicting air quality in advance is crucial for mitigating the detrimental effects of air pollution on public health and economic activities. This study focuses on the development and evaluation of the Nonlinear Autoregressive Exogenous Neural Network (NARX) and Support Vector Machine (SVM) models for multi-step prediction of Malaysia's Air Pollutant Index (API). The models were constructed using a dataset from air quality monitoring stations in Malaysia's three prominent industrial areas: Pasir Gudang, Larkin, and TTDI Jaya. The model development process began by constructing a single-step API predictor, then developing and analyzing a multi-step API predictor using the recursive approach. The prediction performance was assessed using the Root Mean Square Error (RMSE) and Coefficient of Determination values (\mathrm{R}^{2}). The results indicate that the recursive NARX demonstrates promising performance compared to the recursive SVM in multi-step API prediction. However, additional analysis shows that the outliers strongly affected multi-step NARX's prediction, suggesting that the recursive NARX model cannot predict sudden fluctuations not seen in training.
Published in: 2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
Date of Conference: 12-14 September 2023
Date Added to IEEE Xplore: 27 October 2023
ISBN Information: