Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Experimental Phase
2.3. Learning Algorithms
2.3.1. Artificial Neural Network (ANN)
2.3.2. Gated Recurrent Unit (GRU)
2.3.3. Long Short-Term Memory (LSTM)
2.3.4. Random Forest (RF)
2.3.5. Recurrent Neural Network (RNN)
2.3.6. Support Vector Regression (SVR)
2.3.7. Shapley Additive Explanation (SHAP)
2.4. Model Evaluation Metrics
2.5. Features and Testing Scenario
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Types of Data | Variables | Description of Variables |
---|---|---|
Air Quality | PM10, PM2.5, NO2, CO | Mean hourly concentration values (micrograms per cubic meter) |
16D1, 23D0, 23D1, 23D2, 23D3 | 16D1: 24 h concentration averaging period between 16:00 of D1 and 15:00 of D0 23D0: 24 h concentration averaging period between 0:00 and 23:59 of D0 23D1: 24 h concentration averaging period between 0:00 and 23:59 of D1 23D2: 24 h concentration averaging period between 0:00 and 23:59 of D2 23D3: 24 h concentration averaging period between 0:00 and 23:59 of D3 | |
D0, D1, D2, D3 | D0: Prediction Day; D1: Prediction Day Minus One; D2: Prediction Day Minus Two; D3: Prediction Day Minus Three | |
Meteorological Variables | H1000, H850, H700, H500 | Geopotential height of one thousand hectopascal, eight hundred and fifty hectopascal, seven hundred hectopascal, and five hundred hectopascal (meters) |
TAR925, TAR850, TAR700 | Air temperature of nine hundred and twenty-five hectopascal, eight hundred and fifty hectopascal, seven hundred hectopascal (degree Celsius) | |
HR925, HR850, HR700 | Relative humidity of nine hundred and twenty-five hectopascal, eight hundred and fifty hectopascal, seven hundred hectopascal (percentage) | |
TD925, TD850, TD700 | Dew point temperature of nine hundred and twenty-five hectopascal, eight hundred and fifty hectopascal, seven hundred hectopascal (degree Celsius) | |
THI850, THI700, THI500 | Thickness of eight hundred and fifty hectopascal, seven hundred hectopascal, and five hundred hectopascal (meters) | |
STB925, STB850, STB700 | Stability of nine hundred and twenty-five hectopascal, eight hundred and fifty hectopascal, seven hundred hectopascal (degree Celsius) | |
T_AIR_MX, T_AIR_MD, T_AIR_MN | Highest, mean, and lowest air temperature (degree Celsius) | |
HRMX, HRMD, HRMN | Highest, mean, and lowest relative humidity (percentage) | |
TD_MD | Mean dew point temperature (degree Celsius) | |
RRTT | Rainfall (millimeters) | |
VMED | Mean wind speed (meters per second) | |
PREV_WDIR | Predominant wind direction (degree) | |
Other Variables | DD | Number of sunshine hours per day (hour) |
FF | Weekday indicator: non-weekend = 0 and weekend = 1 |
Models | Model Parameters and Hyperparameters | |
---|---|---|
ANN | learning rate | 0.0005 |
epochs | 100 | |
batch_size | 32 | |
validation split | 0.3 | |
GRU | optimizer | adam |
layers | 100 | |
epochs | 500 | |
batch size | 32 | |
LSTM | optimizer | adam |
epochs | 20 | |
batch size | 64 | |
RF | n_estimators | 100 |
criterion | squared error | |
min_sample_split | 2 | |
max_depth | none | |
RNN | optimizer | adam |
layers | 100 | |
epochs | 500 | |
batch size | 32 | |
SVR | kernal | linear |
degree | 3 | |
gamma | scaler |
Categories | Model | Pollutant | Model Performance Indicators | |||
---|---|---|---|---|---|---|
RMSE | MAE | PCC | KTC | |||
Deep Learning Model | ANN | PM10 | 16.15 | 12.63 | 0.84 | 0.67 |
PM2.5 | 6.94 | 5.13 | 0.87 | 0.71 | ||
NO2 | 13.34 | 10.16 | 0.82 | 0.64 | ||
CO | 0.58 | 0.51 | 0.37 | 0.25 | ||
Deep Learning Model | GRU | PM10 | 24.26 | 19.19 | 0.74 | 0.55 |
PM2.5 | 12.18 | 9.19 | 0.73 | 0.56 | ||
NO2 | 15.07 | 12.03 | 0.80 | 0.62 | ||
CO | 0.38 | 0.30 | 0.61 | 0.42 | ||
Deep Learning Model | LSTM | PM10 | 16.69 | 14.20 | 0.88 | 0.70 |
PM2.5 | 9.58 | 8.16 | 0.86 | 0.69 | ||
NO2 | 11.64 | 9.32 | 0.86 | 0.71 | ||
CO | 0.29 | 0.24 | 0.69 | 0.49 | ||
Machine Learning Model | RF | PM10 | 6.81 | 4.86 | 0.96 | 0.84 |
PM2.5 | 2.04 | 1.20 | 0.99 | 0.92 | ||
NO2 | 6.93 | 4.84 | 0.94 | 0.81 | ||
CO | 0.12 | 0.09 | 0.82 | 0.58 | ||
Deep Learning Model | RNN | PM10 | 21.16 | 18.06 | 0.78 | 0.58 |
PM2.5 | 12.76 | 10.35 | 0.67 | 0.48 | ||
NO2 | 19.08 | 15.86 | 0.71 | 0.52 | ||
CO | 0.39 | 0.31 | 0.44 | 0.29 | ||
Machine Learning Model | SVR | PM10 | 7.45 | 5.63 | 0.95 | 0.82 |
PM2.5 | 3.52 | 2.46 | 0.96 | 0.84 | ||
NO2 | 12.19 | 10.12 | 0.92 | 0.77 | ||
CO | 0.28 | 0.24 | 0.72 | 0.51 |
Categories | Model | Pollutant | SD of the Five Model Runs | |||
---|---|---|---|---|---|---|
RMSE | MAE | PCC | KTC | |||
Deep Learning Model | ANN | PM10 | 4.06 | 3.78 | 0.06 | 0.06 |
PM2.5 | 1.02 | 0.76 | 0.03 | 0.03 | ||
NO2 | 2.03 | 1.71 | 0.05 | 0.05 | ||
CO | 0.21 | 0.19 | 0.09 | 0.07 | ||
Deep Learning Model | GRU | PM10 | 6.21 | 5.41 | 0.08 | 0.07 |
PM2.5 | 1.28 | 1.03 | 0.03 | 0.02 | ||
NO2 | 2.31 | 2.00 | 0.04 | 0.05 | ||
CO | 0.06 | 0.06 | 0.04 | 0.03 | ||
Deep Learning Model | LSTM | PM10 | 1.68 | 1.47 | 0.02 | 0.02 |
PM2.5 | 2.54 | 2.29 | 0.03 | 0.04 | ||
NO2 | 2.48 | 2.36 | 0.02 | 0.03 | ||
CO | 0.04 | 0.04 | 0.07 | 0.06 | ||
Machine Learning Model | RF | PM10 | 0.04 | 0.05 | 0.00 | 0.00 |
PM2.5 | 0.03 | 0.01 | 0.00 | 0.00 | ||
NO2 | 0.02 | 0.02 | 0.00 | 0.00 | ||
CO | 0.00 | 0.00 | 0.00 | 0.01 | ||
Deep Learning Model | RNN | PM10 | 2.93 | 2.63 | 0.06 | 0.06 |
PM2.5 | 4.35 | 3.62 | 0.09 | 0.09 | ||
NO2 | 2.08 | 1.96 | 0.04 | 0.04 | ||
CO | 0.10 | 0.08 | 0.04 | 0.02 | ||
Machine Learning Model | SVR | PM10 | 0.00 | 0.00 | 0.00 | 0.00 |
PM2.5 | 0.00 | 0.00 | 0.00 | 0.00 | ||
NO2 | 0.00 | 0.00 | 0.00 | 0.00 | ||
CO | 0.00 | 0.00 | 0.00 | 0.00 |
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Lei, T.M.T.; Cai, J.; Molla, A.H.; Kurniawan, T.A.; Kong, S.S.-K. Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals. Sustainability 2024, 16, 7477. https://doi.org/10.3390/su16177477
Lei TMT, Cai J, Molla AH, Kurniawan TA, Kong SS-K. Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals. Sustainability. 2024; 16(17):7477. https://doi.org/10.3390/su16177477
Chicago/Turabian StyleLei, Thomas M. T., Jianxiu Cai, Altaf Hossain Molla, Tonni Agustiono Kurniawan, and Steven Soon-Kai Kong. 2024. "Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals" Sustainability 16, no. 17: 7477. https://doi.org/10.3390/su16177477