A Novel Hybrid Model Combining the Support Vector Machine (SVM) and Boosted Regression Trees (BRT) Technique in Predicting PM10 Concentration

• Published in: Atmosphere Vol. 13; no. 12; p. 2046
• Main Authors: Shaziayani, Wan Nur, Ahmat, Hasfazilah, Razak, Tajul Rosli, Zainan Abidin, Aida Wati, Warris, Saiful Nizam, Asmat, Arnis, Noor, Norazian Mohamed, Ul-Saufie, Ahmad Zia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Accuracy; Air pollution; Algorithms; boosted regression trees; Carbon monoxide; Feature selection; Machine learning; Methods; Missing data; Modelling; Neural networks; Nitrogen dioxide; Outdoor air quality; Particulate matter; particulate matter (PM10); Pollutants; prediction; Principal components analysis; Regression analysis; support vector machine; Support vector machines; Trees; Variables; Malaysia
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13122046

The PM10 concentration is subject to significant changes brought on by both gaseous and meteorological variables. The aim of this research was to explore the performance of a hybrid model combining the support vector machine (SVM) and the boosted regression trees (BRT) technique in predicting the PM10 concentration for 3 consecutive days. The BRT model was trained by utilizing maximum daily data in the cities of Alor Setar, Klang, and Kuching from the years 2002 to 2017. The SVM–BRT model can optimize the number of predictors and predict PM10 concentration; it was shown to be capable of predicting air pollution based on the models’ performance with NAE (0.15–0.33), RMSE (10.46–32.60), R2 (0.33–0.70), IA (0.59–0.91), and PA (0.50–0.84). This was accomplished while saving training time by reducing the feature size given in the data representation and preventing learning from noise (overfitting) to improve accuracy. This knowledge establishes the foundation for the development of efficient methods to prevent and/or minimize the health effects of PM10 exposure on one’s health.