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https://github.com/saba-gul/forecasting-air-quality-index

Forecasting Particulate Matter Concentration with LSTM. In this project, we explore the use of deep learning models, specifically Long Short-Term Memory (LSTM) networks, to forecast the concentration of particulate matter in two highly polluted regions: Beijing, China and Punjab, Pakistan.
https://github.com/saba-gul/forecasting-air-quality-index

forecasting healthcare lstm-neural-networks particulate-matter pollution recurrent-neural-networks

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Forecasting Particulate Matter Concentration with LSTM. In this project, we explore the use of deep learning models, specifically Long Short-Term Memory (LSTM) networks, to forecast the concentration of particulate matter in two highly polluted regions: Beijing, China and Punjab, Pakistan.

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README 

          
# Forecasting-Air-Quality-Index

## Forecasting Particulate Matter Concentration with LSTM



## Introduction

Particulate matter is a significant contributor to air pollution and climate change, with serious health implications for humans and negative effects on agriculture and the economy. In this project, we explore the use of deep learning models, specifically Long Short-Term Memory (LSTM) networks, to forecast the concentration of particulate matter in two highly polluted regions: Beijing, China and Punjab, Pakistan.



![Air Pollution](images/AQI2.JPG "PM 2.5 & Air Quality Index (AQI) Scale, EPA USA")



## Dataset

We assessed the performance of our network using two datasets from highly polluted regions in South Asia. 



### Beijing, China Dataset

The first dataset pertains to the Beijing region in China and is publicly available on the UCI website (Beijing Data) (Liang et al., 2015), with additional modifications made by (Reddy et al., 2017). The enhanced Beijing air quality dataset includes pollutant data, such as PM2.5 concentration, as well as meteorological parameters like dew point, temperature, pressure, cumulative hours of snow, combined wind direction, cumulative wind speed, and cumulative hours of rain. The dataset spans 35 different stations across Beijing and covers a period of 7 years from 2010 to 2017, comprising a total of 43,825 samples.



![B Pollution](images/Beijing_data.png "t-SNE plot of the modified UCI Beijing air quality dataset")



### Punjab, Pakistan Dataset

The second dataset covers some regions across Punjab with 4 stations across Lahore, which is one of the most polluted cities in the world, and one station each in Multan and Gujranwala respectively. The dataset comprises meteorological parameters acquired from the Pakistan Meteorological Department and pollutant concentration obtained from the Environment Projection Department, Punjab.



![P Pollution](images/Panjab_data.png "t-SNE plot of the Panjab air quality dataset")



## Model Architecture

We employed a recurrent neural network (RNN) with LSTM units for time series forecasting. The LSTM model is known for its ability to capture long-term dependencies in sequential data, making it suitable for time series forecasting tasks. We used Bayesian optimization to tune the hyperparameters of the LSTM model and optimize weight initialization strategies based on the dataset.



## Results

### For Beijing dataset:

  - Next hour forecast RMSE: 0.1913 (91.5% accuracy)

  - Next 24 hours forecast RMSE: 0.7290

    

![R1 Pollution](images/forecast_Beijing.png "Actual Vs. Predicted PM 2.5 values of employed architecture on Hourly data of modified UCI Beijing air quality dataset")



![R2 Pollution](images/forecast_24Beijing.png "Actual Vs. Predicted PM 2.5 values of employed architecture on 24 hour data of modified UCI Beijing air quality dataset")

### For Punjab dataset:

  - Next day forecast RMSE: 0.2192

    

![R3 Pollution](images/forecast_Panjab.png "Actual Vs. Predicted PM 2.5 values of employed architecture on 24 hour data of Panjab air quality dataset")



## Conclusion

The LSTM model shows promising results in forecasting particulate matter concentration, with potential applications in establishing early warning systems based on the air quality index (AQI). These forecasts could enable governments to enact policies aimed at improving air quality and mitigating the health and economic impacts of air pollution.



## Code

The code for the LSTM model and data preprocessing can be found in this repositery.



## References

- Gul S., Khan G.M, Yousaf S. (2022). Multi-step short-term

PM 2.5 forecasting for enactment of proactive

environmental regulation strategies. Environmental Monitoring

and Assessment. 194, 386.(https://doi.org/10.1007/s10661-022-10029-4)



- Gul S., Khan G.M. (2020) Forecasting Hazard Level of Air Pollutants

Using LSTM’s. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial

Intelligence Applications and Innovations. AIAI 2020. IFIP Advances

in Information and Communication Technology, vol 584. Springer,

Cham. https://doi.org/10.1007/978-3-030-49186-4_13



- Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H., & Chen S. X. (2015). Assessing Beijing’s pm2.5 pollution: severity, weather impact, APEC and winter heating. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 471(2182). https://doi.org/10.1098/rspa.2015.0257

Intelligence Applications and Innovations. AIAI 2020. IFIP Advances

in Information and Communication Technology, vol 584. Springer,

Cham. https://doi.org/10.1007/978-3-030-49186-4_13



- Reddy, V., Yedavalli, P., Mohanty, S., & Nakhat, U. (2017). Deep air: forecasting air pollution in Beijing, China. Environmental Science