Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for Atmospheric PM2.5 Forecasting in Bangladesh

• Published in: Atmosphere Vol. 12; no. 1; p. 100
• Main Authors: Shahriar, Shihab Ahmad, Kayes, Imrul, Hasan, Kamrul, Hasan, Mahadi, Islam, Rashik, Awang, Norrimi Rosaida, Hamzah, Zulhazman, Rak, Aweng Eh, Salam, Mohammed Abdus
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Air pollution; ARIMA-ANN; ARIMA-SVM; Artificial neural networks; Atmospheric models; Atmospheric particulate matter; Atmospheric particulates; Autoregressive models; CatBoost; Coronaviruses; COVID-19; Decision trees; deep learning model; DNA; Global health; Learning theory; Neural networks; Nucleotide sequence; Outdoor air quality; Particulate emissions; Particulate matter; Performance assessment; Performance prediction; PM2.5; Public health; Statistical analysis; Support vector machines; Suspended particulate matter; Training; Urban areas; Bangladesh
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos12010100

Atmospheric particulate matter (PM) has major threats to global health, especially in urban regions around the world. Dhaka, Narayanganj and Gazipur of Bangladesh are positioned as top ranking polluted metropolitan cities in the world. This study assessed the performance of the application of hybrid models, that is, Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN), ARIMA-Support Vector Machine (SVM) and Principle Component Regression (PCR) along with Decision Tree (DT) and CatBoost deep learning model to predict the ambient PM2.5 concentrations. The data from January 2013 to May 2019 with 2342 observations were utilized in this study. Eighty percent of the data was used as training and the rest of the dataset was employed as testing. The performance of the models was evaluated by R2, RMSE and MAE value. Among the models, CatBoost performed best for predicting PM2.5 for all the stations. The RMSE values during the test period were 12.39 µg m−3, 13.06 µg m−3 and 12.97 µg m−3 for Dhaka, Narayanganj and Gazipur, respectively. Nonetheless, the ARIMA-ANN and DT methods also provided acceptable results. The study suggests adopting deep learning models for predicting atmospheric PM2.5 in Bangladesh.