Ai Based Air Quality Pm2.5 Forecasting Models for Developing Countries: A Case Study of Ho Chi Minh City, Vietnam

Rakholia, Rajnish; Le, Quan; Ho, Bang Quoc; Vu, Khue Hoang Ngoc; Carbajo, Ricardo Simón

doi:10.2139/ssrn.4111434

Cited by 1 publication

(4 citation statements)

References 15 publications

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“…The performance indexes of the proposed PM predictor in [8] include RMSE = 2.997, MAE = 2.21, and MAPE = 0.039. Rajnish and Quan et al [3] analyzed and discussed the change in PM2.5 concentration over time at different locations in Ho Chi Minh City (HCMC), Vietnam. This study developed several deep learning-based one-shot multi-step PM2.5 forecasting models, an hourly forecast (1h to 24h), and a 24-hour rolling mean.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…This study developed several deep learning-based one-shot multi-step PM2.5 forecasting models, an hourly forecast (1h to 24h), and a 24-hour rolling mean. The accuracy of models was evaluated by RMSE and MAE index and the best performance pertained to SGDRegressor with the lowest average RMSE of 3.38 µg/m 3 and MAE of 2.64 µg/m 3 [3]. According to Wang et al [9], the authors improved neural networks using genetic algorithms.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…In Vietnam, assessing pollution levels is based on parameters like PM2.5, PM10, NO 2 , O 3 , SO 2 , and CO [3]. In this study, we forecast the values of PM2.5 and PM10 based on the previous 15 days" data.…”

Section: B Feature Selectionmentioning

confidence: 99%

“…Air pollution is a serious problem in many countries, especially in developing countries including Vietnam. Air pollution is one of the causes of heart disease, respiratory disease, cardiovascular disease, and lung disease in Vietnam [3]. Hanoi is the capital and the cultural and economic center of Vietnam.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning

Quynh¹,

Viet²,

Thi³

et al. 2023

IJESD

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PM2.5 (Particulate Matter) and PM10 are the most common pollutants, and the increasing of concentration in the air will threaten people’s health. The machine learning method has recently been of particular interest to many researchers due to its effectiveness in air quality prediction models. Many solutions employing deep learning-based techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models to enhance air quality prediction accuracy have been developed. This paper proposes a hybrid Encoder STM model for predicting the next day to the next five days’ PM2.5 and PM10 concentrations in Hanoi. Additionally, we proposed five extended features to increase the accuracy of prediction. Then other models, namely the LSTM model and the Bidirectional LSTM model, are also considered for PM2.5 and PM10 concentration prediction. Our results show that the proposed approaches outperform other state-of-the-art deep learning-based methods on both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) due to low error and the small number of features.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Machine Learning Modelsmentioning

confidence: 99%

Section: B Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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