Toward forecasting future day air pollutant index in Malaysia

Wong, Kok‐Seng; Chew, Yee Jian; Yin, Ooi Shih; Pang, Ying Han

doi:10.1007/s11227-020-03463-z

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The public widely recognizes air quality as a crucial aspect signi cantly impacting their daily lives, comfort, and overall health (Cao et al, 2022;Raunaq et al, 2023). Exposure to these pollutants can cause various health complications for humans (Kampa, 2008;Sentian et al, 2019;Wong et al, 2020). The Ministry of Health Malaysia has reported that respiratory fatality and natural death show signi cant associations with daily mean levels of air pollutants (Usmani et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

SHAFI'I,

JUAHIR

2024

Preprint

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Air pollution poses significant risks to human health, the environment, and the economy. Therefore, striving for cleaner air through efficient air quality monitoring is imperative for fostering a healthier and more sustainable future. Predicting air quality is essential to enhance the quality of life, maintain environmental sustainability, and reduce the economic burden associated with poor air quality issues. The artificial neural network (ANN) is widely recognized as a predominant computational tool in air quality studies due to its capabilities in predicting gaseous and particulate pollutant concentrations, as well as forecasting the air pollutant index (API). This study aimed to investigate the predictive performance of ANN in determining the API by utilizing identified potential sources of air pollutants. Five prediction models were created, namely ANN-PC2018, ANN-PC2019, ANN-PC2020, ANN-PC2021, and ANN-PC2022. Principal component analysis (PCA) was conducted to identify the most significant sources of air pollution, and the results were employed to predict the API using ANN. The ANN-PC2019 model exhibited the highest performance with an R2 value of 0.8612 and RMSE of 7.7467, utilizing four major pollutants as input variables. These findings suggest that forecasting air quality using fewer parameters yields reliable outcomes.

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Section: Introductionmentioning

confidence: 99%

“…The API value is derived by assessing the highest concentration of these pollutants detected over a speci c timeframe. This approach to API calculation is rooted in an averaging method (Wong et al, 2020). The interpretation of air quality, as derived from the API values, is succinctly presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

SHAFI'I,

JUAHIR

2024

Preprint

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Artificial Intelligence: Offline, Online, and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction

Mamat,

Mustakim,

Johari

2024

Internet of Things

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Enhancing Ecosystem Biodiversity Through Air Pollution Concentrations Prediction Using Support Vector Regression Approaches

SOLEHAH,

ABIDIN,

WARRIS

et al. 2023

Int J Conserv Sci

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Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems.

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Toward forecasting future day air pollutant index in Malaysia

Cited by 3 publications

References 26 publications

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

Artificial Intelligence: Offline, Online, and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction

Enhancing Ecosystem Biodiversity Through Air Pollution Concentrations Prediction Using Support Vector Regression Approaches

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