PM2.5 Forecast System by Using Machine Learning and WRF Model, A Case Study: Ho Chi Minh City, Vietnam

Minh, Vo Thi Tam; Tin, Tran Trung; Hiền, Tô Thị

doi:10.4209/aaqr.210108

Cited by 23 publications

(2 citation statements)

References 35 publications

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“…Unlike a purely statistical model, machine learning considers multiple parameters for prediction, increasing the accuracy of the result. A recent study in Vietnam developed daily average PM2.5 forecasting models for HCM City, this daily PM2.5 forecasting used six machine learning algorithms and gave a conclusion that the Extra Trees Regression model gives the best forecast with statistical evaluation indicators including RMSE = 7.68 µg/m 3 , MAE = 5.38 µg/m 3 , R-squared = 0.68, and the confusion matrix accuracy of 74% [7]. The authors of [8] used a spatiotemporal Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) model to predict the next day's daily average PM2.5 concentrations in Beijing City using data collected over three years from January 1st, 2015 to December 31st, 2017.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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%

Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning

Quynh¹,

Viet²,

Thi³

et al. 2023

IJESD

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“…Numerical modeling entails numerically converting meteorological conditions, air pollution emissions, and traffic volumes to a model for forecasting. The numerical modeling method includes chemical transport models [8], weather research and forecasting models [9], weather research and forecasting models coupled with chemistry [10], weather research, and forecasting community multi-scale air quality models [11]. The numerical method is powerful for modeling air quality with detailed spatial and temporal resolution and complex chemical and physical modeling.…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

Kim

Jung

et al. 2023

Atmosphere

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Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.

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