Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations

• Published in: Journal of chemometrics Vol. 38; no. 4
• Main Authors: Taşağıl Arslan, Gökçe Nur, Kılıç Depren, Serpil
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.04.2024
• Subjects: Air pollution; Air quality; Carcinogens; Deep learning; Discrete Wavelet Transform; Health problems; Health risks; hybrid model; long short‐term memory; Outdoor air quality; Particulate emissions; Particulate matter; PM10; Pollutants; Quality of life; Sustainable development; Teaching methods; Urbanization; wavelet transform; Wavelet transforms; Istanbul Turkey; Turkey
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3539

The rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper planning of future activities. In this manner, this study considers Istanbul, a province with a high concentration of industry, population, and vehicle traffic. Particulate matter (PM), one of the most basic air pollutants, is stated to contain microscopic solids or liquid droplets that are small enough to be inhaled and cause serious health problems. Thus, it is recommended to apply discrete wavelet transform (DWT) and deep learning method long short‐term memory (LSTM) as a hybrid model to predict the concentration of PM10. Using the mentioned methods, they can predict air pollution to have been developed within the scope of this study. Furthermore, the hybrid approach with LSTM by selecting the most appropriate discrete wavelet type emphasizes the difference of this study from the existing literature. The ability of these developed methods to make successful future predictions helps institutions and organizations that can take precautions on the subject to take action at the right time; in addition, the deep learning methods used contribute to the development of sustainable smart environmental systems. In today's environment when air pollution is increasing and threatening human health, any precaution that can be taken would improve the quality of life for all living things, reduce health issues and deaths caused by air pollution, and thus raise the degree of well‐being. These findings might offer a reliable scientific evidence for Istanbul City's air pollution management, which can serve as an example for other regions. This study investigates the importance of wavelet type selection as a determinant factor in the performance of DWT‐LSTM models. Among the wavelet types considered, the Db10 from the Daubechies wavelet family consistently emerges as a standout performer. A compelling narrative of superior performance unfolds in evaluating the hybrid DWT‐LSTM model against the LSTM method. The DWT‐LSTM model exhibits a notable reduction in error metrics and captures intricate patterns and nuances within the PM10 concentration data indicating its robust predictive capabilities.