Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania
Abstract
:1. Introduction
Ref. | A Brief Description | Objective Function | Data Location and Source | Predictors | Time-Series | Strengths | Limitation |
---|---|---|---|---|---|---|---|
[17] | An automated air quality forecasting system is developed for daily forecasts based on five various ML models: MLR, MLP, RF, GBDT, and SVR, combined with an FS technique. | PM2.5, PM10, SO2, NO2, O3, CO | Seven cities in China: Beijing, Shanghai, Guangzhou, Chengdu, Xi’an, Wuhan, and Changchun. (http://www.cnemc.cn) (accessed on 15 January 2022). | Daily pressure, 2 m temperature, relative humidity, precipitation, visibility, and total cloud cover. (http://data.cma.cn) (accessed on 15 January 2022). | Daily average | Development of an automated air quality forecasting system based on five various ML models. | Feature importance scores were calculated by the RF model, in which the predictor variables were checked individually. |
[14] | Hybrid model based on a deterministic prediction module (RF-ELM) combined with an interval prediction module. | PM2.5 | Three major cities in China are Guang Zhou, Shenzhen, and Zhuhai. | ---- | Daily average | The use of an interval prediction module. | These are very complex models. |
[15] | RF model combined with a meteorological normalization method. | PM2.5 | Hubei Province, China. https://quotsoft.net/air/ (accessed on 15 January 2022). | Included 2 m temperature, 2 m dewpoint temperature, 10 m u-component of wind, 10 m v-component of wind, surface pressure, total precipitation, boundary layer height, and downward surface solar radiation. | Hourly | The use of a meteorological normalization method. | Only a quantification of air pollution was performed. No forecasting and/or modelling was made. |
[16] | Hybrid air quality forecasting system based on relief-F algorithm combined with a MOCBO and a modified fuzzy neural network. | AQI | Shanghai, Hangzhou, and Nanjing are three regions with severe air pollution in China. | PM2.5, PM10, SO2, CO, NO2, and O3 concentrations, average temperature (°C), cumulative precipitation (CP, mm), average wind speed (AWS, m/s), and average relative humidity. | Daily average | A comparison with other ML models and FS methods. | One combination of inputs was found for AQI forecasting. |
[13] | MDA, Bagged CART, and RF combined with SA. | PM10 | A total of 75 stations over Barcelona, Spain. | Minimum temperature, maximum temperature, normalized difference vegetation index, precipitation, wind speed, wind direction, elevation, road density, topographic wetness index, land use, terrain roughness index, distance from water body, land use, and lithology. | Annually average | The use of many FS-ML models and comparison with others. | One combination of inputs was found for PM10 forecasting. |
[18] | An ANN model was used to forecast daily pollutant concentrations. Real-time correlation (RTC) was applied to improve the quality of the forecasts. | PM10, PM2.5, NO2, and O3 | A total of 32 continuous air-quality-monitoring stations in Delhi, India. | CAVG_DAY0 CAVG_DAYM1 BLH_DAYN T2M_DAYN RH_DAYN IS975_DAYN IS950_DAYN IS925_DAYN U10_DAYNM1_DAYN V10_DAYNM1_DAYN TP_DAYN FIRE_DAYNM3_DAYNM1. | Daily average | Application of Real-Time Correction (RTC) technique. | ANN is a stochastic method, which means that one cannot obtian the same results for the same dataset. No FS was applied. |
[19] | A hybrid early-warning artificial intelligence framework (ICEEMDAN-OS-ELM) was proposed. | PM2.5, PM10, and lower atmospheric visibility | Gladstone, Brisbane, Mackay Region, Newcastle, and Sydney, Australia. | --- | Hourly | The results are benchmarked with many ML models. | The main common weakness is that one should have data (measures) for obtaining data (forecasts). |
[20] | Forecasting AQI using a long short-term memory (LSTM) neural network model combined with a variational mode decomposition (VMD) and a sample entropy. | AQI | Beijing and Baoding, China. https://www.aqistudy.cn/historydata/ (accessed on 15 January 2022). | --- | Daily average | A comparison with other models was performed. | No FS was applied. |
[21] | Air pollutant concentration forecasting was performed by combining an EWT decomposition algorithm with MAEGA and NARX neural networks. | PM2.5, SO2, NO2, CO | Beijing in China. | --- | --- | A comparison was made with the VMD-MAEGA-NARX, EWT-MAEGA-SVM, MAEGA-NARX, EWT-NARX, and EWT-ARIMA-NARX models. | No inputs and no FS were applied. |
[22] | A dynamic multiple equation (DME) model (a linear model). | PM2.5 | Santiago, Chile. | Temperature, wind speed, relative humidity, wind direction, and CO. | Hourly and daily average | A comparison with SARI-MAX and ANN models. | Complex model structure. |
- i.
- Implementing an Autonomous Anomaly Detection method during data preprocessing to identify and exclude anomalous data points.
- ii.
- Identifying spatial and temporal hazards detected by the study’s sensors/stations.
- iii.
- Clustering and decomposing data based on the significance of AQI in terms of health implications.
- iv.
- Analyzing partial dependence and estimating the importance of each predictor variable considered.
- v.
- Determining the optimal combinations of predictor variables for predicting AQI and other related pollutant concentrations through a comprehensive FS approach.
- vi.
- Evaluating the performance of five hybrid FS-ML models for predicting a one-minute series of PM10, PM2.5, and PM1 and then AQI.
- vii.
- Developing new physical models for estimating PM10, PM2.5, PM1, and AQI.
- viii.
- Creating a new interface module to provide PM10, PM2.5, PM1, and AQI predictions based on the provided predictor variables.
- i.
- Analyzing pollution episodes in Craiova in line with World Health Organization (WHO) recommendations.
- ii.
- Evaluating the correlations between meteorological parameters, AQI, and PM concentrations and interrelations among different PM fractions, such as PM1, PM2.5, and PM10.
- iii.
- Investigating the influences of noise and carbon dioxide (CO2) on PM concentrations.
2. Data and Statistical Analysis
2.1. Local Weather Information
2.2. Correlation between the PM1, PM2.5, and PM10 Concentrations
2.3. Evaluation Criteria and Statistical Indices
3. Hybrid FS-ML Models
3.1. Machine Learning Models
- i.
- Artificial Neural Network
- ii.
- Support Vector Machine
- iii.
- Decision Tree
- iv.
- Gaussian Process Regression
- v.
- Linear Regression
3.2. Feature Selection: Integral Feature Selection Method
3.3. Modelling: Least Square Regression
4. Methodology
- Start the algorithm.
- Import the inputs and outputs data.
- First, the data are pre-processed by applying normalization and Autonomous Anomaly Detection, are loaded to each studied ML model, and then are subdivided into training (80% of data) and testing (the remaining data).
- Compute the total number of combinations based on the data size loaded using Equation (11).
- Start a first loop based on the size of the provided data, K1.
- Compute the number of combinations for each ith considered size and then start a second loop for each value of K2.
- Use the combnk(V, K) function for producing a matrix with K columns.
- Load the ML model, load the data, and compute the considered output parameter.
- Save the computed values and go to the next iteration.
- After obtaining the predicted values by all considered combinations, the result is imported by a second algorithm in which the statistical analysis is performed.
- The best combinations of inputs are found and then the algorithm is ended.
5. Results and Discussion
- -
- Comb1: Temperature
- -
- Comb2: Pressure
- -
- Comb3: Humidity
- -
- Comb4: Temperature and Pressure
- -
- Comb5: Temperature and Humidity
- -
- Comb6: Pressure and Humidity
- -
- Comb7: Temperature, Pressure, and Humidity
5.1. The Hybrid FS-DT Model Applied for Predicting PM1 Concentrations
5.2. Hybrid FS-DT Model Applied for Predicting PM2.5 Concentrations
5.3. Hybrid FS-DT Model Applied for Predicting PM10 Concentrations
5.4. Influence of VOC, Noise, and CO2 on PM Concentrations
5.5. Modelling of PMs and AQI
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Station | PM1 (μg/m3) | PM2.5 (μg/m3) | PM10 (μg/m3) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
a1 | a2 | a3 | a0 | a1 | a2 | a3 | a0 | a1 | a2 | a3 | a0 | |
820002C3 | 0 | 2.59 × 10−3 | −0.11 | −12.54 | 0 | 3.43 × 10−3 | −0.13 | −17.13 | 0 | 3.85 × 10−3 | −0.16 | −18.09 |
1600020A | 0.14 | 6.65 × 10−6 | 0 | 1.48 | 0.26 | −1.7 × 10−5 | 0.03 | 0.72 | 0.27 | 8.14 × 10−6 | 0 | 0.87 |
1600020B | 0 | −3.01 × 10−5 | 0.06 | 4.98 | 0 | −1.02 × 10−3 | 0.19 | 6.07 | 0 | −1.42 × 10−3 | 0.27 | 6.13 |
1600020C | 0 | 1.06 × 10−5 | −0.05 | 9.64 | 0 | 3.03 × 10−5 | −0.10 | 13.41 | 0 | 3.66 × 10−5 | −0.12 | 14.50 |
1600020D | 0 | 5.02 × 10−6 | −0.03 | 9.63 | 1.07 × 10−2 | 1.2 × 10−5 | −0.06 | 13.69 | 0 | 2.05 × 10−5 | −0.09 | 16.02 |
1600020E | 1.69 | −7.59 × 10−3 | 0.93 | −0.70 | 2.84 | −1.26 × 10−2 | 1.57 | −4.61 | 3.27 | −1.39 × 10−2 | 1.67 | −2.58 |
1600020F | 0.22 | 2.25 × 10−5 | 0 | −1.46 | 1.83 | −1.17 × 10−2 | 1.43 | 0.13 | 2.08 | −1.36 × 10−2 | 1.66 | 0.23 |
1600023A | 0.48 | 1.56 × 10−6 | 0 | −0.24 | 1.9 | −6.21 × 10−3 | 0.75 | 0.33 | 0 | 5.53 × 10−3 | −0.20 | −33.02 |
16000207 | 0 | −1.37 × 10−3 | −0.12 | 33.86 | 0 | −9.95 × 10−5 | −0.25 | 36.49 | 0 | −3.10 × 10−5 | −0.24 | 30.10 |
16000208 | 0 | −5.64 × 10−5 | 0.19 | −1.26 | 0 | −1.66 × 10−3 | 0.39 | −1.65 | 0 | −2.13 × 10−3 | 0.48 | −1.87 |
16000209 | 0 | 3.21 × 10−3 | −0.19 | −14.23 | 0.61 | 1.83 × 10−3 | −0.19 | −6.74 | 0 | 5.81 × 10−3 | −0.34 | −27.24 |
16000238 | 1.41 | −4.75 × 10−3 | 0.56 | 1.45 | 0.91 | −5.60 × 10−5 | 0.12 | −1.59 | 2.80 | −1.03 × 10−2 | 1.27 | 1.51 |
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Abbreviation | Nomenclature | Units |
---|---|---|
ANNs | Artificial Neural Networks | -- |
LCE | Legate’s Coefficient of Efficiency | Dimensionless |
LSR | Least Square Regression | -- |
MAPE | Mean Absolute Percentage Error | In percentage |
MBE | Mean Bias Error | μg/m3 |
EWT | Ensemble Wavelet Transform | -- |
VMD | Variational Mode Decomposition | -- |
NARX | Network nonlinear Autoregressive Network with Exogenous Inputs | -- |
ARIMA | Auto Regressive Integrated Moving Average Model | -- |
MAEGA | Multi-Agent Evolutionary Genetic Algorithm | -- |
ELM | General Neural Networks | -- |
LSTM | Deep Learning Neural Networks | -- |
MODA | Multi-objective Dragonfly Optimization Algorithm | -- |
MOPSO | Multi-objective Article Swarm Optimization Algorithm | -- |
MOBO | Multi-objective Bonobo Optimizer | -- |
PM | Particle Matter Concentration | μg/m3 |
R2 | Coefficient of Determination | Dimensionless |
ML | Machine Learning | -- |
RH | Relative Humidity | In percentage |
P | Pressure | Pa |
RMSE | Root Mean Square Error | μg/m3 |
SBF | Slope of Best-Fit line | Dimensionless |
FS | Feature Selections | -- |
T | Temperature | °C |
TS | Test Statistic | Dimensionless |
WIA | Willmott’s Index of Agreement | Dimensionless |
σ | Standard Deviation | μg/m3 |
φ | Performance Score | Dimensionless |
MDA | Mixture Discriminant Analysis | -- |
Bagged CART | Bagged Classification and Regression Trees | -- |
RF | Random Forest | -- |
SA | Simulated Annealing Method | -- |
SVM | Support Vector Machine | -- |
DT | Decision Tree | -- |
GPR | Gaussian Process Regression | -- |
LR | Linear Regression | -- |
RF-ELM | Random Fourier Extreme Learning Machine | -- |
RF-ELM | Random Fourier Extreme Learning Machine | -- |
OS-ELM | Online Sequential Extreme Learning Machine | -- |
IVS | Input Variable Selection | -- |
AQI | Air Quality Conditions for Health |
---|---|
0–50 | Good |
51–100 | Moderate |
101–150 | Unhealthy for sensitive groups |
151–200 | Unhealthy |
201–300 | Very unhealthy |
301–500 | Hazardous |
Input and Output Number | Parameter | Unit |
---|---|---|
Input 1 | Temperature | °C |
Input 2 | Pressure | Pa |
Input 3 | Relative Humidity | % |
Input 4 | NOISE | ---- |
Input 5 | CO2 | μg/m3 |
Input 6 | VOC | ---- |
Output 1 | PM1 | μg/m3 |
Output 2 | PM2.5 | μg/m3 |
Output 3 | PM10 | μg/m3 |
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El Mghouchi, Y.; Udristioiu, M.T.; Yildizhan, H. Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania. Sensors 2024, 24, 1532. https://doi.org/10.3390/s24051532
El Mghouchi Y, Udristioiu MT, Yildizhan H. Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania. Sensors. 2024; 24(5):1532. https://doi.org/10.3390/s24051532
Chicago/Turabian StyleEl Mghouchi, Youness, Mihaela Tinca Udristioiu, and Hasan Yildizhan. 2024. "Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania" Sensors 24, no. 5: 1532. https://doi.org/10.3390/s24051532
APA StyleEl Mghouchi, Y., Udristioiu, M. T., & Yildizhan, H. (2024). Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania. Sensors, 24(5), 1532. https://doi.org/10.3390/s24051532